cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Quantum response to time-dependent external fields
Creators: name: Miyashita, Seiji
name: Tanaka, Shu
name: De Raedt, Hans
name: Barbara, Bernard
Publisher: name: arXiv
Year: 2008
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Disordered Systems and Neural Networks (cond-mat.dis-nn)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Recently, explicit real time dynamics has been studied in various systems. These quantum mechanical dynamics could provide new recipes in information processing. We study quantum dynamics under time dependent external fields, and explore how to control the quantum state, and also how to bring the state into a target state. Here, we investigate a pure quantum mechanical dynamics, dynamics in quantum Monte Carlo simulation and also in quantum master equation. For the control magnetic states, operators which do not commute with magnetization are important. We study case of the transverse Ising model, in which we compare natures of thermal and quantum fluctuations. We also study the cases of the Dzyaloshinsky-Moriya interaction, where we find a peculiar energy level structure. Moreover we study the case of itinerant magnetic state, where we study the change from the Mott insulator to the Nagaoka ferromagnetic state. Effects of dissipation are also discussed.
description: 10 pages, 7 figures, Proceedings of International Workshop on Statistical-Mechanical Informatics 2008
Relationships between the velocity of the ice hockey wrist shot and selected human factors
Creators: name: Moyls, Peter William
Publisher: name: University of British Columbia
Year: 2010

description: The main purpose of this study was to investigate the relationships between the velocity of the ice hockey wrist shot and selected human factors in order to be able to predict the velocity of the wrist shot. A second objective was to identify the modifiable human factors most highly related with puck velocity in order to guide further research investigating the causal factors of a high speed wrist shot. Thirty-five subjects were selected from junior, intermediate, collegiate, senior and professional leagues to take part in the study. Their ages ranged from seventeen to twenty-nine. Forty-eight- strength, flexibility, muscle ratio, and anthropometric variables were measured in the Buchanan Fitness Centre at U.B.C. Puck velocity was measured by the use of a radar gun at the Thunderbird arena. Each subject shot a minimum of five shots and the top three scores were averaged to give the puck velocity score. With this data, Stepwise Regression and All Possible Subsets Regression techniques were used to find the best possible regression equation for predicting puck velocity. The product-moment correlations of puck velocity with each variable were tested for significance at the .05 level. The following correlations were significant: arm adduction at the shoulder of the lower arm, forearm supination of the upper arm, wrist strength in extension of the upper arm, wrist strength in flexion of the lower arm, and diagonal arm strength of the lower arm. The best linear regression equation found for predicting puck velocity was: [equations not included] Several transformations were performed on the data in an attempt to reduce the amount of uncertainty in the prediction equation. It was found that cubing the strength variables led to a new regression equation which accounted for more of the uncertainty and used fewer variables. The best regression equation using transformed data was: [equations not included] This equation had a multiple R of .871 and a standard error of estimate of 3.74. The transformed data equation reduced the uncertainty by 10% with one fewer variable being required. It is recommended that the variables with a significant correlation with puck velocity and the variables in the untransformed data equation be investigated further by implementing them into a training program for hockey players. In this way it might be empirically verified that these factors are the causes of a fast shot.
Optical and IR observations of SN 2002dj: some possible common properties of fast expanding SNe Ia
Creators: name: Pignata, G.
name: Benetti, S.
name: Mazzali, P. A.
name: Kotak, R.
name: Patat, F.
name: Meikle, P.
name: Stehle, M.
name: Leibundgut, B.
name: Suntzeff, N. B.
name: Buson, L. M.
name: Cappellaro, E.
name: Clocchiatti, A.
name: Hamuy, M.
name: Maza, J.
name: Mendez, J.
name: Ruiz-Lapuente, P.
name: Salvo, M.
name: Schmidt, B. P.
name: Turatto, M.
name: Hillebrandt, W.
Publisher: name: arXiv
Year: 2008
Subjects: subject: Astrophysics (astro-ph)
subject: FOS: Physical sciences

description: As part of the European Supernova Collaboration we obtained extensive photometry and spectroscopy of the type Ia SN 2002dj covering epochs from 11 days before to nearly two years after maximum. Detailed optical and near-infrared observations show that this object belongs to the class of the high-velocity gradient events as indicated by Si, S and Ca lines. The light curve shape and velocity evolution of SN 2002dj appear to be nearly identical to SN 2002bo. The only significant difference is observed in the optical to near-IR colours and a reduced spectral emission beyond 6500 A. For high-velocity gradient Type Ia supernovae, we tentatively identify a faster rise to maximum, a more pronounced inflection in the V and R light curves after maximum and a brighter, slower declining late-time B light curve as common photometric properties of this class of objects. They also seem to be characterized by a different colour and colour evolution with respect to ``normal'' SNe Ia. The usual light curve shape parameters do not distinguish these events. Stronger, more blueshifted absorption features of intermediate-mass elements and lower temperatures are the most prominent spectroscopic features of Type Ia supernovae displaying high velocity gradients. It appears that these events burn more intermediate-mass elements in the outer layers. Possible connections to the metallicity of the progenitor star are explored.
description: Equations A4, A5 and A7 in the appendix section have been corrected. Part of text in the appendix has been removed
Work
Creators: name: Priyanshu, Aman
Publisher: name: figshare
Year: 2021
Subjects: subject: 170203 Knowledge Representation and Machine Learning
subject: FOS: Psychology

description: EEG technology and analysis offers a great opportunity for developing Brain-Computer-Interface. Machine learning shows great potential in this subject of study, however, the subject of study is of great sensitivity. BCI or EEG analysis has been under heavy discussion and scrutiny with respect to privacy concerns and ethics. Leakage of data, which may include personal emotions and sentiments as well as ethical concerns with misrepresentation of learnt data by ML models. Previous studies on EEG analysis have focused on decoding emotions, hand-movements and thought representation. However, these studies do not focus on contributor privacy and therefore this work attempts to provide a proof-of-concept for employing Federated Learning for conserving the same. The analysis provided employs EEG data collected for Hand Movement Classification from 4 different clients. The results demonstrate the effectiveness of Federated Learning as an aggregating tool for EEG analysis. Achieving an accuracy of 57.67% which is comparable to 61.83% when utilizing the complete dataset. The study focuses on privacy and the employment of federated learning as a means of overcoming it.
Persistent random walk on a one-dimensional lattice with random asymmetric transmittances
Creators: name: Sadjadi, Zeinab
name: Miri, MirFaez
Publisher: name: arXiv
Year: 2008
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Soft Condensed Matter (cond-mat.soft)
subject: FOS: Physical sciences

description: We study the persistent random walk of photons on a one-dimensional lattice of random asymmetric transmittances. Each site is characterized by its intensity transmittance t (t') for photons moving to the right (left) direction. Transmittances at different sites are assumed independent, distributed according to a given probability density Distribution. We use the effective medium approximation and identify two classes of probability density distribution of transmittances which lead to the normal diffusion of photons. Monte Carlo simulations confirm our predictions.
description: 7 pages, submitted to Phys. Rev. E
Post-Pandemic Growth in 9-1-1 Paramedic Calls and Emergency Department Transports Surpasses Pre-Pandemic Rates in the COVID-19 Era: Implications for Paramedic Resource Planning
Creators: name: Strum, Ryan P.
name: McLeod, Brent
name: Mondoux, Shawn
name: Miller, Paul
name: Costa, Andrew P.
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Medicine
subject: Molecular Biology
subject: Evolutionary Biology
subject: FOS: Biological sciences
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Mental Health
subject: Infectious Diseases
subject: FOS: Health sciences

description: The COVID-19 pandemic led to a decline in emergency department (ED) visits and a subsequent return to baseline pre-pandemic levels. It is unclear if this trend extended to paramedic services and if patient cohorts accessing paramedics changed. We examined trends and associations between paramedic utilization (9-1-1 calls and ED transports) and the COVID-19 timeframe. We conducted a retrospective cross-sectional study using paramedic call data from the Hamilton Paramedic Services from January 2016 to December 2023. We included all 9-1-1 calls where paramedics responded to an incident, excluding paramedic interfacility transfers. We calculated lines of best fit for the pre-pandemic period (January 2016 to January 2020) and compared their predictions to the actual volumes in the post-pandemic period (May 2021 to December 2023). We used an interrupted time series regression model to determine the association between pandemic timeframes (pre-, during-, post-COVID-19) and paramedic utilization (9-1-1 calls and ED transports), while testing for annual seasonality. During the study timeframe, 577,278 calls for paramedics were received and 413,491 (71.6%) were transported to the ED. Post-pandemic, 9-1-1 calls exceeded predicted pre-pandemic levels by 1,298 per month, while ED transports exceeded by 543 per month. The pandemic significantly reduced monthly 9-1-1 calls (-588.2, 95% CI −928.8 to −247.5) and ED transports (-677.3, 95% CI −927.0 to −427.5). Post-pandemic, there was a significant and sustained resurgence in monthly 9-1-1 calls (1,208.0, 95% CI 822.1 to 1,593.9) and ED transports (868.8, 95% CI 585.8 to 1,151.7). Both models exhibited seasonal variations. Post-pandemic, 9-1-1-initiated paramedic calls experienced a substantial increase, surpassing pre-pandemic growth rates. ED transports returned to pre-pandemic levels but with a steeper and continuous pattern of growth. The resurgence in paramedic 9-1-1 calls and ED transports post-COVID-19 emphasizes an urgent necessity to expedite development of new care models that address how paramedics respond to 9-1-1 calls and transport to overcrowded EDs.
Relative entropy of entanglement for certain multipartite mixed states
Creators: name: Wei, Tzu-Chieh
Publisher: name: arXiv
Year: 2008
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: We prove conjectures on the relative entropy of entanglement (REE) for two families of multipartite qubit states. Thus, analytic expressions of REE for these families of states can be given. The first family of states are composed of mixture of some permutation-invariant multi-qubit states. The results generalized to multi-qudit states are also shown to hold. The second family of states contain Dür's bound entangled states. Along the way, we have discussed the relation of REE to two other measures: robustness of entanglement and geometric measure of entanglement, slightly extending previous results.
description: Single column, 22 pages, 9 figures, comments welcome
Creating Opioid Response Specialists: A Harm Reduction Initiative
Creators: name: Broach, John P.
name: Rettger, Brian
name: Gigliotti, Ronald
name: Chapman, Brittany P.
name: Joseph, Jillian
name: Smiley, Abbey
name: Hunter, Michael
name: Soucie, Norman
name: Gross, Karen
name: Babu, Kavita M.
name: Weisberg, Stacy N.
Publisher: name: Taylor & Francis
Year: 2025
Subjects: Medicine
Neuroscience
Pharmacology
Biotechnology
Mental Health

description: Aim: Each year, more than 100,000 Americans die from an overdose. Most of these deaths are attributed to high-potency opioids, including fentanyl. People who use drugs face multiple barriers to treatment including access, knowledge of options, and adverse medical experiences. In 2022, Worcester County, Massachusetts saw a sharp increase in overdose deaths. In response, our team, based in Central Massachusetts developed and implemented an Opioid Response Specialist (ORS) Program, under the auspices of a mobile addiction service which has been operating in our area since 2021. Methods: The Mobile Addiction Service is composed of physicians and advanced practice providers to facilitate low-barrier access to medications for opioid use disorder (MOUD) and harm reduction resources and the ORS service includes two care givers with emergency medical services (EMS) experience, currently also working as paramedics in the area. A model curriculum for the ORS role was developed and then the pilot testing of this role within the existing mobile addiction service was performed. These specialists were trained to the paramedic level and added extensive experience with overdose reversal, phlebotomy, wound care, and additional touch points with high-risk patients to the mobile service. Results: As a result, we saw increases in naloxone and clean syringe distribution, and hepatitis C and human immunodeficiency virus screening. The integration of these personnel within the mobile addiction service also facilitated the uptake of opioid use disorder treatment interventions by our city’s EMS which resulted in further improvements to city-wide naloxone distribution and the use of buprenorphine/naloxone after overdose reversal. Conclusions: Based on our initial work, the concept of an ORS, especially when embedded with a mobile addiction service, has the potential to improve access to harm reduction as well as form the basis of a training program to extend the skills and scope of personnel with a background in EMS practice.
Factors Associated with Abusive Head Trauma in Young Children Presenting to Emergency Medical Services Using a Large Language Model
Creators: name: Broad, Allison
name: Luo, Xiao
name: Tahabi, Fattah Muhammad
name: Abdoo, Denise
name: Zhang, Zhan
name: Adelgais, Kathleen
Publisher: name: Taylor & Francis
Year: 2025
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences

description: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data. Our goal was to apply AI and LLM to EMS narrative documentation of young children to detect AHT. This is a retrospective cohort study of EMS transports of children <36 months of age with a diagnosis of head injury from the 2018–2019 ESO Research Data Collaborative. Non-abusive closed head injury (NA-CHI) was distinguished from AHT and child maltreatment (AHT-CAN) through 2 expert reviewers; kappa statistic (k) assessed inter-rater reliability. A Natural Language Processing (NLP) framework using an LLM augmented with expert derived n-grams was developed to identify AHT-CAN. We compared test characteristics (sensitivity, specificity, negative predictive value (NPV)) between this NLP framework to a Generative Pretrained Transformer (GPT) or n-grams only models to detect AHT-CAN. Association of specific word tokens with AHT-CAN was analyzed using Pearson’s chi-square. Area Under the Receiver Operator Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC) are also reported. There were 1082 encounters in our cohort; 1030 (95.2%) NA-CHI and 52 (4.8%) AHT-CAN. Inter-rater agreement was substantial (k = 0.71). The augmented NLP framework had a specificity and sensitivity of 72.4% and 92.3%, respectively with a NPV of 99.5%. In comparison, the GPT model had a sensitivity of 69.2%, specificity of 97.1% and NPV of 98.4% and n-grams alone had a sensitivity of 53.8%, specificity of 62.0%, NPV of 96.4%. AUROC was 0.91 and AUPRC was 0.52. A total of 44 n-grams and bi-grams were positively associated with AHT-CAN including “domestic,” “various,” “bruise,” “cheek,” “multiple,” “doa,” “not respond,” “see EMS.” AI and LLMs have high sensitivity and specificity to detect AHT-CAN in EMS free-text narratives. Words associated with physical signs of trauma are strongly associated with AHT-CAN. LLMs augmented with a list of n-grams may help EMS identify signs of trauma that aid in the detection of AHT in young children.
Factors associated with abusive head trauma in young children presenting to emergency medical services using a large language model
Creators: name: Broad, Allison
name: Luo, Xiao
name: Tahabi, Fattah Muhammad
name: Abdoo, Denise
name: Zhang, Zhan
name: Adelgais, Kathleen
Publisher: name: Taylor & Francis
Year: 2025
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences

description: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data. Our goal was to apply AI and LLM to EMS narrative documentation of young children to detect AHT. This is a retrospective cohort study of EMS transports of children <36 months of age with a diagnosis of head injury from the 2018-2019 ESO Research Data Collaborative. Non-abusive closed head injury (NA-CHI) was distinguished from AHT and child maltreatment (AHT-CAN) through 2 expert reviewers; kappa statistic (k) assessed inter-rater reliability. A Natural Language Processing (NLP) framework using an LLM augmented with expert derived n-grams was developed to identify AHT-CAN. We compared test characteristics (sensitivity, specificity, negative predictive value (NPV)) between this NLP framework to a Generative Pretrained Transformer (GPT) or n-grams only models to detect AHT-CAN. Association of specific word tokens with AHT-CAN was analyzed using Pearson's chi-square. Area Under the Receiver Operator Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC) are also reported. There were 1082 encounters in our cohort; 1030 (95.2%) NA-CHI and 52 (4.8%) AHT-CAN. Inter-rater agreement was substantial (k= 0.71). The augmented NLP framework had a specificity and sensitivity of 72.4% and 92.3%, respectively with a NPV of 99.5%. In comparison, the GPT model had a sensitivity of 69.2%, specificity of 97.1% and NPV of 98.4% and n-grams alone had a sensitivity of 53.8%, specificity of 62.0%, NPV of 96.4%. AUROC was 0.91 and AUPRC was 0.52. A total of 44 n-grams and bi-grams were positively associated with AHT-CAN including “domestic”, “various”, “bruise”, “cheek”, “multiple”, “doa”, “not respond”, “see EMS”. AI and LLMs have high sensitivity and specificity to detect AHT-CAN in EMS free-text narratives. Words associated with physical signs of trauma are strongly associated with AHT-CAN. LLMs augmented with a list of n-grams may help EMS identify signs of trauma that aid in the detection of AHT in young children.
Evidence for stellar contamination in the transmission spectra of HAT-P-12b
Creators: name: Jiang, C.
name: Chen, G.
name: Palle, E.
name: Murgas, F.
name: Parviainen, H.
name: Yan, F.
name: Ma, Y.
Publisher: name: arXiv
Year: 2021
Subjects: subject: Earth and Planetary Astrophysics (astro-ph.EP)
subject: Solar and Stellar Astrophysics (astro-ph.SR)
subject: FOS: Physical sciences

description: Transmission spectroscopy characterizes the wavelength dependence of transit depth, revealing atmospheric absorption features in planetary terminator regions. In this context, different optical transmission spectra of HAT-P-12b reported in previous studies exhibited discrepant atmospheric features (e.g., Rayleigh scattering, alkali absorption). We aim to understand the atmosphere of HAT-P-12b using two transit spectroscopic observations by the Gran Telescopio CANARIAS (GTC), and to search for evidence of stellar activity contaminating the transmission spectra, which might be the reason behind the discrepancies. We used Gaussian processes to account for systematic noise in the transit light curves and used nested sampling for Bayesian inferences. We performed joint atmospheric retrievals using the two transmission spectra obtained by GTC OSIRIS, as well as previously published results, coupled with stellar contamination corrections for different observations. The retrieved atmospheric model exhibits no alkali absorption signatures, but shows tentative molecular absorption features including $\rm H_2O$, $\rm CH_4$ and $\rm NH_3$. The joint retrieval of the combined additional public data analysis retrieves similar results, but with a higher metallicity. Based on Bayesian model comparison, the discrepancies of the transmission spectra of HAT-P-12b can be explained by the effect of different levels of unocculted stellar spots and faculae. In addition, we did not find strong evidence for a cloudy or hazy atmosphere from the joint analysis, which is inconsistent with prior studies based on the observations of Hubble Space Telescope.
description: 17 pages, 14 figures, accepted for publication in A&A
Stochastic approach for a multivalued Dirichlet-Neumann problem
Creators: name: Maticiuc, Lucian
name: Rascanu, Aurel
Publisher: name: arXiv
Year: 2008
Subjects: subject: Dynamical Systems (math.DS)
subject: Analysis of PDEs (math.AP)
subject: FOS: Mathematics
subject: 35D05, 35K85, 60H10, 60H30, 47J20

description: We prove the existence and uniqueness of a viscosity solution of the parabolic variational inequality with a nonlinear multivalued Neumann-Dirichlet boundary condition:% {equation*} \{{array}{r} \dfrac{\partial u(t,x)}{\partial t}-\mathcal{L}_{t}u(t,x) {+}{% \partial ϕ}\big(u(t,x)\big)\ni f\big(t,x,u(t,x),(\nabla uσ)(t,x)\big), t>0, x\in \mathcal{D},\medskip \multicolumn{1}{l}{\dfrac{\partial u(t,x)}{\partial n}+{\partial ψ}\big(% u(t,x)\big)\ni g\big(t,x,u(t,x)\big), t>0, x\in Bd(\mathcal{D}%),\multicolumn{1}{l}{u(0,x)=h(x), x\in \bar{\mathcal{D}},}% {array}%. {equation*}% where $\partial ϕ$ and $\partial ψ$ are subdifferentials operators and $\mathcal{L}_{t}$ is a second differential operator. The result is obtained by a Feynman-Kaç representation formula starting from the backward stochastic variational inequality:% {equation*} \{{array}{l} dY_{t}{+}F(t,Y_{t},Z_{t}) dt{+}G(t,Y_{t}) dA_{t}\in \partial ϕ(Y_{t}) dt{+}\partial ψ(Y_{t}) dA_{t}{+}Z_{t}dW_{t}, 0\leq t\leq T,\medskip \ Y_{T}=ξ.% {array}%. {equation*}
description: 29 pages
Hydrodynamical Simulations of the Triggering of Nuclear Activities by Minor Mergers of Galaxies
Creators: name: Yang, Chao
name: Ge, Junqiang
name: Lu, Youjun
Publisher: name: arXiv
Year: 2021
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: Major mergers of galaxies are considered to be an efficient way to trigger Active Galactic Nuclei and are thought to be responsible for the phenomenon of quasars. This has however recently been challenged by observations of a large number of low luminosity Active Galactic Nuclei at low redshift ($z\lesssim1$) without obvious major merger signatures. Minor mergers are frequently proposed to explain the existence of these Active Galactic Nuclei. In this paper, we perform nine high resolution hydrodynamical simulations of minor galaxy mergers and investigate whether nuclear activities can be efficiently triggered by minor mergers, by setting various properties for the progenitor galaxies of those mergers. We find that minor galaxy merger scan activate the massive black hole in the primary galaxy with an Eddington ratio of $f_{\rm Edd}>0.01$ and $>0.05$ (or a bolometric luminosity $>10^{43}$ and $>10^{44}\mathrm{erg\, s^{-1}}$) with a duration of $2.71$ and $0.49$ Gyr (or $2.69$ and $0.19$ Gyr), respectively. The nuclear activity of primary galaxy strongly depends on the nucleus separation, the nucleus is more active as the two nuclei approach to each other. Dual Active Galactic Nuclei systems can still possibly form by minor mergers of galaxies, the time period for dual Active Galactic Nuclei is only $\sim 0.011$ Gyr and $\sim 0.017$ Gyr with Eddington ratio of $f_{\rm Edd}>0.05$ and bolometric luminosity $>10^{44}\mathrm{erg\, s^{-1}}$. This time period is typically shorter than that of dual Active Galactic Nuclei induced by galaxy major mergers.
description: RAA published, 18 pages, 7 figures. Format problems fixed
Accuracy of Commercial Large Language Model (ChatGPT) to Predict the Diagnosis for Prehospital Patients Suitable for Ambulance Transport Decisions: Diagnostic Accuracy Study
Creators: name: Miller, Eric D.
name: Franc, Jeffrey Michael
name: Hertelendy, Attila J.
name: Issa, Fadi
name: Hart, Alexander
name: Woodward, Christina A.
name: Newbury, Bradford
name: Newbury, Kiera
name: Mathew, Dana
name: Whitten-Chung, Kimberly
name: Bauer, Eric
name: Voskanyan, Amalia
name: Ciottone, Gregory R.
Publisher: name: Taylor & Francis
Year: 2025
Subjects: Neuroscience
Biotechnology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Cancer
Science Policy
Mental Health
Hematology

description: While ambulance transport decisions guided by artificial intelligence (AI) could be useful, little is known of the accuracy of AI in making patient diagnoses based on the pre-hospital patient care report (PCR). The primary objective of this study was to assess the accuracy of ChatGPT (OpenAI, Inc., San Francisco, CA, USA) to predict a patient’s diagnosis using the PCR by comparing to a reference standard assigned by experienced paramedics. The secondary objective was to classify cases where the AI diagnosis did not agree with the reference standard as paramedic correct, ChatGPT correct, or equally correct. This diagnostic accuracy study used a zero-shot learning model and greedy decoding. A convenience sample of PCRs from paramedic students was analyzed by an untrained ChatGPT-4 model to determine the single most likely diagnosis. A reference standard was provided by an experienced paramedic reviewing each PCR and giving a differential diagnosis of three items. A trained prehospital professional assessed the ChatGPT diagnosis as concordant or non-concordant with one of the three paramedic diagnoses. If non-concordant, two board-certified emergency physicians independently decided if the ChatGPT or the paramedic diagnosis was more likely to be correct. ChatGPT-4 diagnosed 78/104 (75.0%) of PCRs correctly (95% confidence interval: 65.3–82.7%). Among the 26 cases of disagreement, judgment by the emergency physicians was that in 6/26 (23.0%) the paramedic diagnosis was more likely to be correct. There was only one case of the 104 (0.96%) where transport decisions based on the AI guided diagnosis would have been potentially dangerous to the patient (under-triage). In this study, overall accuracy of ChatGPT to diagnose patients based on their emergency medical services PCR was 75.0%. In cases where the ChatGPT diagnosis was considered less likely than paramedic diagnosis, most commonly the AI diagnosis was more critical than the paramedic diagnosis—potentially leading to over-triage. The under-triage rate was <1%.
Creating Opioid Response Specialists: A Harm Reduction Initiative
Creators: name: Broach, John P
name: Rettger, Brian
name: Gigliotti, Ronald
name: Chapman, Brittany P
name: Joseph, Jillian
name: Smiley, Abbey
name: Hunter, Michael
name: Soucie, Norman
name: Gross, Karen
name: Babu, Kavita M
name: Weisberg, Stacy N
Publisher: name: Taylor & Francis
Year: 2025
Subjects: Medicine
Neuroscience
Pharmacology
Biotechnology
Mental Health

description: Each year, more than 100,000 Americans die from an overdose. Most of these deaths are attributed to high-potency opioids, including fentanyl. People who use drugs face multiple barriers to treatment including access, knowledge of options, and adverse medical experiences. In 2022, Worcester County, Massachusetts saw a sharp increase in overdose deaths. In response, our team, based in Central Massachusetts developed and implemented an Opioid Response Specialist (ORS) Program, under the auspices of a mobile addiction service which has been operating in our area since 2021. The Mobile Addiction Service is composed of physicians and advanced practice providers to facilitate low-barrier access to medications for opioid use disorder (MOUD) and harm reduction resources and the ORS service includes two care givers with emergency medical services (EMS) experience, currently also working as paramedics in the area. A model curriculum for the ORS role was developed and then the pilot testing of this role within the existing mobile addiction service was performed. These specialists were trained to the paramedic level and added extensive experience with overdose reversal, phlebotomy, wound care, and additional touch points with high-risk patients to the mobile service. As a result, we saw increases in naloxone and clean syringe distribution, and hepatitis C and human immunodeficiency virus screening. The integration of these personnel within the mobile addiction service also facilitated the uptake of opioid use disorder treatment interventions by our city’s EMS which resulted in further improvements to city-wide naloxone distribution and the use of buprenorphine/naloxone after overdose reversal. Based on our initial work, the concept of an ORS, especially when embedded with a mobile addiction service, has the potential to improve access to harm reduction as well as form the basis of a training program to extend the skills and scope of personnel with a background in EMS practice.
Compliance Evaluation with ChatGPT for Diagnosis and Treatment in Patients Brought to the ED with a Preliminary Diagnosis of Stroke
Creators: name: Yazla, Merve
name: Sarcan, Emine
Publisher: name: Taylor & Francis
Year: 2025
Subjects: Medicine
Neuroscience
Biotechnology
Cancer

description: Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing product developed by OpenAI. Recently, the use of ChatGPT has gained attention in the field of health care, particularly for its potential applications in diagnostic and decision-making support. While its utility is still being explored, it shows promise as a supplementary tool in these contexts. This study aims to evaluate the potential of ChatGPT in making decisions about ‘transportation to the stroke center, suspicion of large vessel occlusion and treatment decisions’ of patients brought to the emergency department by ambulance with a preliminary diagnosis of stroke. All patients with a stroke code who were transferred to the emergency department (ED) of a tertiary care hospital, Ankara Etlik City Hospital, by ambulance between November 1, 2023, and April 30, 2024, during designated stroke team coverage periods were included in the study. Unlike many stroke centers that operate continuously 24/7, our institution follows a structured on-call system, where specialized stroke teams are assigned time slots to provide stroke care. Data were collected from prehospital records, ED notes, and hospital imaging and treatment records. ChatGPT’s decisions were compared to gold standard outcomes using Cohen’s kappa test, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) calculated for each directive. A total of 512 patients were analyzed, and ChatGPT’s decisions were compared with the patients’ final diagnoses and treatments. Analysis comparing ChatGPT’s decisions to patient outcomes across prehospital stroke suspicion, large vessel occlusion diagnosis, and treatment phases showed significant agreement (p < 0.001, Kappa: 0.540–0.562). While the sensitivity of the diagnosis of stroke was 91%, the NPV was found to be 98% in patients requiring intravenous tissue plasminogen activator and large vessel occlusion, 97% NPV in patients requiring mechanical thrombectomy. ChatGPT shows promise as a decision-support tool for identifying acute ischemic stroke and determining treatment needs in prehospital and ED settings. However, its reliance on predefined data highlights the need for physician supervision to address clinical complexities and ensure patient safety. Integrating ChatGPT as an adjunct rather than a standalone system can enhance decision-making efficiency while maintaining high-quality care.
Splenic artery embolization for the management of severe life-threatening warm autoimmune hemolytic anemia
Creators: name: Egbaria, Aya
name: Bisharat, Naiel
Publisher: name: Taylor & Francis
Year: 2025
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Molecular Biology
subject: Pharmacology
subject: Sociology
subject: FOS: Sociology
subject: Immunology
subject: FOS: Clinical medicine
subject: Science Policy

description: Urgent splenectomy is recommended for the management of highly transfusion-dependent life-threatening warm autoimmune hemolytic anemia, while for individuals unfit for surgery, splenic embolization is an alternative treatment option. However, reports on its use in this context are sparse, and data on patient outcomes, effectiveness, and safety remain limited. A 21-year-old female patient presented with severe transfusion-dependent, life-threatening, warm autoimmune hemolytic anemia that was unresponsive to steroids and rituximab, reaching a nadir of 2.3 g/dL. Due to the severity of the anemia, she was unfit for surgery and therefore she underwent embolization of the lower two-thirds of the spleen parenchyma. Within 24 h after embolization, her hemoglobin level increased to 4.9 g/dL. A literature search yielded eight other reports describing the use of splenic embolization in severe warm autoimmune hemolytic anemia. No procedure-related deaths were reported. While some complications may require invasive interventions (e.g. splenic abscess drainage, splenectomy), the improvement in blood counts following splenic embolization often facilitated safer conditions for these procedures. Based on this report and review of the literature, the use of splenic embolization in cases of transfusion-dependent, life-threatening, warm autoimmune hemolytic anemia is safe and is associated with marked improvement in hemoglobin levels.
A Quality Improvement Initiative to Increase Confirmation of Prehospital Endotracheal Tube Placement at Emergency Department Transfer of Care
Creators: name: Kwong, Jonathan L
name: Bourn, Scott
name: Hillier, Morgan
name: Merko, Mike
name: Grass, AJ
name: Ednie, Tim
name: Verbeek, P Richard
Publisher: name: Taylor & Francis
Year: 2024
Subjects: subject: Neuroscience
subject: Physiology
subject: FOS: Biological sciences
subject: Information Systems not elsewhere classified
subject: Cancer
subject: Science Policy

description: OBJECTIVES: Rates of prehospital unplanned extubation (UE) range from 0-25% and are the result of many factors, including patient movement. Transfer of care of intubated patients to the emergency department (ED) involves significant patient movement and represents a high-risk event for UE. Frequent confirmation of endotracheal tube (ETT) placement is imperative for early recognition of UE and to minimize patient harm. METHODS: Local Practice - Our baseline rate of verbal ETT position confirmation with a member of the ED team during ED transfer of care was 74%. Our goal was to increase this practice to >90% in six months. This project was completed in partnership with Toronto Paramedic Services. Prehospital electronic patient care records (ePCRs) were reviewed weekly to determine the proportion of intubated patients who had ETT placement confirmed in the ED at transfer of care. Interventions - Pre- and post-project paramedic focus groups were conducted to identify potential drivers, change ideas, and project feedback. Three staggered interventions were introduced over five months: 1) memorandums to paramedics, ED chiefs and respiratory therapy leads, 2) individualized paramedic feedback e-mails, and 3) ePCR changes and closing rules. RESULTS: The pre-project focus group identified several potential drivers such as physical barriers, interprofessional relationships, and communication. ETT confirmation remained ≥90% for the last eight weeks and interventions resulted in special cause variation. Median cases without verbal confirmation between paramedics and ED staff reduced from 5/week (IQR 2.5, 6.5) to 1/week (IQR 0, 2). UE was identified in 0.6% (2/340) of patients with ETT confirmation. The post-project focus group noted improvements in perceived accountability, interprofessional relationships and satisfaction with interventions. CONCLUSION: Through a series of interventions, we improved the rate of ETT confirmation during ED transfer of care. Although rates of UE were low, improvement in ETT confirmation may lead to faster recognition of UE when it does occur thereby mitigating complications. The observed improvement was sustained after interventions ended.
The First-Order Velocity Memory Effect from Compact Binary Coalescing Sources
Creators: name: Divakarla, Atul K.
name: Whiting, Bernard F.
Publisher: name: arXiv
Year: 2021
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: It has long been known that gravitational waves from compact binary coalescing sources are responsible for a first-order displacement memory effect experienced by a pair of freely falling test masses. This constant displacement is sourced from the non-vanishing final gravitational-wave strain present in the wave's after-zone, often referred to as the non-linear memory effect, and is of the same order of magnitude as the strain from the outgoing quadrupole radiation. Hence, this prediction of general relativity is verifiable experimentally by measurement of the final relative separation between test masses that comprise gravitational-wave detectors. In a separate context, independent calculations have demonstrated that exact, sandwich, plane wave spacetimes exhibit a velocity memory effect: a non-zero relative velocity, gained by a pair of test masses in free fall, after the passage of a gravitational wave. In this paper, we find that in addition to the known constant displacement memory effect test masses experience, a velocity memory effect at leading order arises due to the non-linear nature of gravitational waves from compact binary sources. We discuss the magnitude of the first-order velocity memory effect in the context of observing gravitational-wave radiation from super massive binary black hole mergers in LISA.
Testing a new method for scattering in finite volume in the $ϕ^4$ theory
Creators: name: Garofalo, Marco
name: Romero-López, Fernando
name: Rusetsky, Akaki
name: Urbach, Carsten
Publisher: name: arXiv
Year: 2021
Subjects: subject: High Energy Physics - Lattice (hep-lat)
subject: FOS: Physical sciences

description: We test an alternative proposal by Bruno and Hansen [1] to extract the scattering length from lattice simulations in a finite volume. For this, we use a scalar $ϕ^4$ theory with two mass nondegenerate particles and explore various strategies to implement this new method. We find that the results are comparable to those obtained from the Lüscher method, with somewhat smaller statistical uncertainties at larger volumes.
description: 4 pages, 3 figures
Accuracy of commercial large language model (ChatGPT) to predict the diagnosis for prehospital patients suitable for ambulance transport decisions: Diagnostic accuracy study
Creators: name: Miller, Eric D.
name: Franc, Jeffrey Michael
name: Hertelendy, Attila J.
name: Issa, Fadi
name: Hart, Alexander
name: Woodward, Christina A.
name: Newbury, Bradford
name: Newbury, Kiera
name: Mathew, Dana
name: Whitten-Chung, Kimberly
name: Bauer, Eric
name: Voskanyan, Amalia
name: Ciottone, Gregory R.
Publisher: name: Taylor & Francis
Year: 2025
Subjects: Neuroscience
Biotechnology
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Cancer
Science Policy
Mental Health
Hematology

description: While ambulance transport decisions guided by artificial intelligence (AI) could be useful, little is known of the accuracy of AI in making patient diagnoses based on the pre-hospital patient care report (PCR). The primary objective of this study was to assess the accuracy of ChatGPT (OpenAI, Inc., San Francisco, CA, USA) to predict a patient's diagnosis using the PCR by comparing to a reference standard assigned by experienced paramedics. The secondary objective was to classify cases where the AI diagnosis did not agree with the reference standard as paramedic correct, ChatGPT correct, or equally correct. This diagnostic accuracy study used a zero-shot learning model and greedy decoding. A convenience sample of PCRs from paramedic students was analyzed by an untrained ChatGPT-4 model to determine the single most likely diagnosis. A reference standard was provided by an experienced paramedic reviewing each PCR and giving a differential diagnosis of three items. A trained prehospital professional assessed the ChatGPT diagnosis as concordant or non-concordant with one of the three paramedic diagnoses. If non-concordant, two board-certified emergency physicians independently decided if the ChatGPT or the paramedic diagnosis was more likely to be correct. ChatGPT-4 diagnosed 78/104 (75.0%) of PCRs correctly (95% confidence interval: 65.3% to 82.7%). Among the 26 cases of disagreement, judgment by the emergency physicians was that in 6/26 (23.0%) the paramedic diagnosis was more likely to be correct. There was only one case of the 104 (0.96%) where transport decisions based on the AI guided diagnosis would have been potentially dangerous to the patient (under-triage). In this study, overall accuracy of ChatGPT to diagnose patients based on their emergency medical services PCR was 75.0%. In cases where the ChatGPT diagnosis was considered less likely than paramedic diagnosis, most commonly the AI diagnosis was more critical than the paramedic diagnosis - potentially leading to over-triage. The under-triage rate was less than 1%.
CANGAROO-III Search for Gamma Rays from Kepler's Supernova Remnant
Creators: name: Enomoto, R.
name: Higashi, Y.
name: Yoshida, T.
name: Tanimori, T.
Publisher: name: arXiv
Year: 2008
Subjects: subject: Astrophysics (astro-ph)
subject: FOS: Physical sciences

description: Kepler's supernova, discovered in October 1604, produced a remnant that has been well studied observationally in the radio, infrared, optical, and X-ray bands, and theoretically. Some models have predicted a TeV gamma-ray flux that is detectable with current Imaging Cherenkov Atmospheric Telescopes. We report on observations carried out in 2005 April with the CANGAROO-III telescope. No statistically significant excess was observed, and limitations on the allowed parameter range in the model are discussed.
description: 6 pages 6 figures, to appear in ApJ, August 20, 2008, v683n2
Compliance Evaluation with ChatGPT for diagnosis and treatment in patients brought to the ED with a Preliminary Diagnosis of Stroke
Creators: name: Yazla, Merve
name: Sarcan, Emine
Publisher: name: Taylor & Francis
Year: 2025
Subjects: Medicine
Neuroscience
Biotechnology
Cancer

description: Chat Generative Pre-trained Transformer (ChatGPT) is a natural language processing product developed by OpenAI. Recently, the use of ChatGPT has gained attention in the field of healthcare, particularly for its potential applications in diagnostic and decision-making support. While its utility is still being explored, it shows promise as a supplementary tool in these contexts. This study aims to evaluate the potential of ChatGPT in making decisions about 'transportation to the stroke center, suspicion of large vessel occlusion and treatment decisions' of patients brought to the emergency department by ambulance with a preliminary diagnosis of stroke. All patients with a stroke code who were transferred to the emergency department (ED) of a tertiary care hospital, Ankara Etlik City Hospital, by ambulance between November 1, 2023, and April 30, 2024, during designated stroke team coverage periods were included in the study. Unlike many stroke centers that operate continuously 24/7, our institution follows a structured on-call system, where specialized stroke teams are assigned time slots to provide stroke care. Data were collected from prehospital records, ED notes, and hospital imaging and treatment records. ChatGPT's decisions were compared to gold standard outcomes using Cohen's kappa test, with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) calculated for each directive. A total of 512 patients were analyzed, and ChatGPT's decisions were compared with the patients' final diagnoses and treatments. Analysis comparing ChatGPT's decisions to patient outcomes across prehospital stroke suspicion, large vessel occlusion diagnosis, and treatment phases showed significant agreement (p < 0.001, Kappa: 0.540-0.562). While the sensitivity of the diagnosis of stroke was 91%, the NPV was found to be 98% in patients requiring intravenous tissue plasminogen activator and large vessel occlusion, 97% NPV in patients requiring mechanical thrombectomy. ChatGPT shows promise as a decision-support tool for identifying acute ischemic stroke and determining treatment needs in prehospital and ED settings. However, its reliance on predefined data highlights the need for physician supervision to address clinical complexities and ensure patient safety. Integrating ChatGPT as an adjunct rather than a standalone system can enhance decision-making efficiency while maintaining high-quality care.