bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving large language models for clinical named entity recognition via prompt engineering
Authors: Hu Yan , Chen Qingyu , Du Jingcheng , Peng Xueqing , Keloth Vipina Kuttichi , Zuo Xu , Zhou Yujia , Li Zehan , Jiang Xiaoqian , Lu Zhiyong , Roberts Kirk , Xu Hua 
Published: January 2024

Abstract

Importance
The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models’ performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

Objectives
This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

Materials and Methods
We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) to identify nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT.

Results
Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all 4 components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed.

Discussion
The study’s findings suggest a promising direction in leveraging LLMs for clinical NER tasks. However, while the performance of GPT models improved with task-specific prompts, there's a need for further development and refinement. LLMs like GPT-4 show potential in achieving close performance to state-of-the-art models like BioClinicalBERT, but they still require careful prompt engineering and understanding of task-specific knowledge. The study also underscores the importance of evaluation schemas that accurately reflect the capabilities and performance of LLMs in clinical settings.

Conclusion
While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications.
Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: a case study of the MIMIC-IV Emergency Department module
Authors: van Damme Philip , Löbe Matthias , Benis Nirupama , de Keizer Nicolette F , Cornet Ronald 
Published: January 2024

Abstract

Objectives
To provide a real-world example on how and to what extent Health Level Seven Fast Healthcare Interoperability Resources (FHIR) implements the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles for scientific data. Additionally, presents a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR.

Materials and methods
A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department (MIMIC-ED) dataset, a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators.

Results
The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified.

Discussion
Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR.

Conclusion
To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures.
Design and evaluation of an electronic prospective medication order review system for medication orders in the inpatient setting
Authors: Ojha Pooja , Anderson Benjamin J , Draper Evan W , Flaker Susan M , Siska Mark H , Mara Kristin C , Kennedy Brian D , Schreier Diana J 
Published: January 2024

Abstract

Objectives
Since the 1970s, a plethora of tools have been introduced to support the medication use process. However, automation initiatives to assist pharmacists in prospectively reviewing medication orders are lacking. The review of many medications may be protocolized and implemented in an algorithmic fashion utilizing discrete data from the electronic health record (EHR). This research serves as a proof of concept to evaluate the capability and effectiveness of an electronic prospective medication order review (EPMOR) system compared to pharmacists’ review.

Materials and methods
A subset of the most frequently verified medication orders were identified for inclusion. A team of clinical pharmacist experts developed best-practice EPMOR criteria. The established criteria were incorporated into conditional logic built within the EHR. Verification outcomes from the pharmacist (human) and EPMOR (automation) were compared.

Results
Overall, 13 404 medication orders were included. Of those orders, 13 133 passed pharmacist review, 7388 of which passed EPMOR. A total of 271 medication orders failed pharmacist review due to order modification or discontinuation, 105 of which passed EPMOR. Of the 105 orders, 19 were duplicate orders correctly caught by both EPMOR and pharmacists, but the opposite duplicate order was rejected, 51 orders failed due to scheduling changes.

Discussion
This simulation was capable of effectively discriminating and triaging orders. Protocolization and automation of the prospective medication order review process in the EHR appear possible using clinically driven algorithms.

Conclusion
Further research is necessary to refine such algorithms to maximize value, improve efficiency, and minimize safety risks in preparation for the implementation of fully automated systems.
Decline in muscle strength and physical function after fracture in men – the prospective STRAMBO study
Authors: Szulc Paweł , Lewis Joshua R , Chapurlat Roland 
Published: January 2024

Abstract
Studies on muscle strength and physical function after fracture are focused on short follow-ups and adjacent anatomical region. We compared loss of muscle strength and physical function in men after fracture with normal ageing-related decline. In 823 men aged 60–87, measurements of grip strength and clinical tests (chair stands, balance) were performed every 4 years for 12 years. In 155 men with incident fracture, we compared the status after vs. before the fracture. In men without fracture (controls), we compared the status on the first follow-up (4 years) vs. baseline. In men with fracture, grip strength decreased more than in the controls (41%, 0.28SD, P &lt; .01). Men with fracture had higher risk of incident deterioration on the five chair-stand test vs. the controls (OR = 2.45, P &lt; .001). They had higher risk of incident inability to stand for 10s with closed eyes vs. the controls (OR = 4.80, P &lt; .01). They also had higher risk of deterioration on the tandem walk than the controls: forwards (OR = 2.04, P &lt; .01), backwards (OR = 2.25, P &lt; .005). The rapid physical decline was not limited to the region of the fracture site. In men who had incident non-upper limb fractures, grip strength decreased more (32%, P &lt; .05) vs. the controls. In men who had incident non-lower limb fractures, the risk of decline in the tests of the lower limbs was higher vs. controls (chair stands, OR = 2.73, P &lt; .001). The risk of decline was higher in men with clinical fractures which occurred &gt;1 year before the next visit vs. controls (tandem walk forwards, OR = 2.98, P &lt; .005). Overall, in older men, fractures were associated with greater loss of muscle strength and physical function vs. normal ageing. This accelerated decline was also found in the anatomical regions remote from the fracture site. Thus, programs to decrease or reverse the post-fracture decline could have beneficial effects on subsequent fracture risk.
An effective Q extraction method via deep learning
Authors: Li Fang , Yu Zhenzhen , Ma Jianwei 
Published: January 2024

Abstract
The quality factor (Q) is a parameter reflecting the physical properties of reservoirs. Accurate estimation of the quality factor plays an important role in improving the resolution of seismic data. Spectral-ratio method is a widely used traditional method based on the linear least-squares fitting to extract the quality factor, but is sensitive to noise. This is the main reason preventing this method from being widely used. Some supervised deep-learning methods are proposed to extract Q in which the construction of training labels is a key link. The proposed method is based on the spectral-ratio method to create training labels, avoiding errors in generating them. In contrast to the least-squares method, this paper proposes to use a nonlinear regression algorithm based on a fully connected network to fit the spectral logarithmic ratio and frequency. Meanwhile, the empirical equation is applied to constrain prediction results. The proposed method can effectively overcome the influence of noise and improve the accuracy of prediction results. Tests on the synthesized data of vertical seismic profile and common middle profile show that the proposed method has better generalization ability than the spectral-ratio method. Applying the method to the field vertical seismic profile data successfully extracts the quality factor, which can provide effective information for dividing stratigraphic layers.
Effect of temperature and fluid on rock microstructure based on an effective-medium theory
Authors: Zhu Hesong , Ba Jing , Zhang Lin , Carcione José M , Yan Xinfei 
Published: January 2024

Abstract
Temperature and pressure variations during the geologic diagenesis process can lead to complex pore structures in tight rocks. The effective-medium theory, based on the stress–strain relationship in combination with pore structure parameters, can be used to describe the elastic-wave responses of rocks. In this work, the differential effective medium (DEM) and self-consistent approximation (SCA) models are combined to invert the pore-crack spectrum. The Voigt–Reuss–Hill average is used to estimate the elastic moduli of the minerals. Then, based on SCA, the pore structures are incorporated into the rock matrix to create a new host phase. Subsequently, the DEM theory is used to add cracks with different volume fractions and aspect ratios to the host phase. To predict the structure of pores and cracks (crack density and aspect ratio), an objective function is defined as the sum of variances between experimentally measured and predicted wave velocities. The results show that the modeling predictions of P- and S-wave velocities at different temperatures and pressure ratios agree well with the experimental measurements. Variations in pore structure are determined at a zero effective pressure and different temperatures. We analyze the characteristics of how cracks change with variations in temperature and confining pressure, providing a theoretical basis for characterizing the structure of tight rocks.
Sound absorption mechanism of underwater anechoic coating with spherical cavities
Authors: Hu Ningdong , Jin Jun , Zou Wentao , Peng Weicai , Hu Hongping 
Published: January 2024

Abstract
An axisymmetric theoretical model is simplified to investigate sound absorption in the underwater anechoic coating with spherical cavities. An empirical equation is put forward to predict the frequency at the first absorption peak. The sound absorption mechanism of viscoelastic solid with cavity is clarified by the power analysis method. The quantitative analysis of viscoelastic power dissipation on sound energy provides a new idea for the structural topology optimization of anechoic coatings. These formulas are given to calculate the dissipation power of viscoelastic solid. The dependence of sound absorption performance of the anechoic coating upon radius and position of single or two spherical cavities of the unit cell is analyzed in detail. The study provides guidance for the design of anechoic coatings with cavities.
Returning to sport after injury: the influence of injury appraisals and post-traumatic stress symptoms on adolescent risk-taking intentions post-injury
Authors: Seasons Mackenzie , Morrongiello Barbara A 
Published: January 2024

Abstract

Objective
How youth think about injury risk can affect their decisions about whether to engage in behaviors that can lead to injury. Appraisals also influence the development of post-traumatic stress symptoms (PTSS), which occur in approximately 20% of children after a medically treated injury. The current study examined how the injury appraisals of youth are associated with the development of PTSS post-skateboarding injury, and if PTSS or perceived benefits of the sport are also associated with youths’ intentions to return to the sport.

Method
One hundred three youth who had a medically treated skateboarding injury within the last year provided survey data on injury appraisals, PTSS, the benefits of skateboarding, and intentions to return to the sport.

Results
A two-stage moderated statistical mediation path model was specified. In the first stage, there was a positive relationship between pain at injury and PTSS, which was attenuated by the moderator, perceived bad luck. PTSS fully mediated the association between perceived pain at the time of injury and intentions to return to skateboarding. In the second stage of the mediation model, the moderator perceived benefits of skateboarding, reversed the negative relationship between PTSS and intentions to return to skateboarding.

Conclusions
Skateboarders are a group at risk for injury that can lead to PTSS, and they also are likely to return to the sport despite PTSS. This research identifies factors that impact the decision to return to skateboarding after injury. Limitations of the study and implications for mental health support and injury prevention are provided.
A case of rare lumbar intradural tumor: paraganglioma
Authors: Sevin Ismail Ertan , Dağ Onur Davut , Kahraman Aslı , Sucu Hasan Kamil 
Published: January 2024

Abstract
Lumbar paragangliomas are rare neuroendocrine neoplasms arising from specialized neural crest cells in the cauda equina/filum terminale region. They are difficult to diagnose radiologically and can be difficult to treat surgically if they secrete catecholamines. A 38-year-old woman presented with three and a half years of increasing lower back and sacrum discomfort. Her neurological examination was normal. The MRI revealed an L4 intradural lesion that was compressing the cauda equina. A total tumor resection was conducted. The paraganglioma was diagnosed by the pathology report. Paragangliomas should be considered in the differential diagnosis of intradural masses of the lumbar spine.
Modeling in Sample Surveys: Discussion of Professor Valliant’s Hansen Lecture 2022
Authors: Raghunathan Trivellore 
Published: January 2024

AbstractStatistical modeling has been, and continues to be, at the core of statistical inferential activity. No good can come off of sophisticated modeling of data from a badly designed study, and hence, the design principles have been at the core of our field. However, use of models in sample surveys has been controversial and has been limited to certain applications, such as small-area estimation. It is clear, however, from Professor Valliant’s paper that almost every practical problem in sample surveys requires modeling. In my view, the distrust of modeling strategy, limited to a certain period, is largely due to a misconstrued threat to probability sample designs. The historical account and contributions of Hansen have deeper implications for future development in sample survey research and in training, ultimately integrating modeling and design principles for survey inferences.
Deep learning approach for identification of H ii regions during reionization in 21-cm observations – II. Foreground contamination
Authors: Bianco Michele , Giri Sambit K , Prelogović David , Chen Tianyue , Mertens Florent G , Tolley Emma , Mesinger Andrei , Kneib Jean-Paul 
Published: January 2024

ABSTRACT
The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination that pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift z = 7 and 11. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2’s independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with ${\rm AUC}=95~{{\ \rm per\ cent}}$ is possible for 8 &lt; z &lt; 10. While the network prediction lacks the ability to correctly identify ionized regions at higher redshift and differentiate well the few remaining neutral regions at lower redshift due to low contrast between 21-cm signal, noise, and foreground residual in images. Moreover, as the photon sources driving reionization are expected to be located inside ionized regions, we show that SegU-Net v2 can be used to correctly identify and measure the volume of isolated bubbles with $V_{\rm ion}\gt (10\, {\rm cMpc})^3$ at z &gt; 9, for follow-up studies with infrared/optical telescopes to detect these sources.
Determining the baryon impact on the matter power spectrum with galaxy clusters
Authors: Grandis Sebastian , Aricò Giovanni , Schneider Aurel , Linke Laila 
Published: January 2024

ABSTRACT
The redistribution of baryonic matter in massive haloes through processes like active galactic nuclei feedback and star formation leads to a suppression of the matter power spectrum on small scales. This redistribution can be measured empirically via the gas and stellar mass fractions in galaxy clusters, and leaves imprints on their electron density profiles. We constrain two semi-analytical baryon correction models with a compilation of recent Bayesian population studies of galaxy groups and clusters sampling a mass range above ∼3 × 1013 M⊙, and with cluster gas density profiles derived from deep, high-resolution X-ray observations. We are able to fit all the considered observational data, but highlight some anomalies in the observations. The constraints allow us to place precise, physically informed priors on the matter power spectrum suppression. At a scale of k = 1 h Mpc−1 we find a suppression of $0.042^{+0.012}_{-0.014}$ ($0.049^{+0.016}_{-0.012}$), while at k = 3 h Mpc−1 we find $0.184^{+0.026}_{-0.031}$ ($0.179^{+0.018}_{-0.020}$), depending on the model used. In our fiducial setting, we also predict at 97.5 per cent credibility, that at scales k &lt; 0.37 h Mpc−1 baryon feedback impacts the matter power less than 1 per cent. This puts into question if baryon feedback is the driving factor for the discrepancy between cosmic shear and primary CMB results. We independently confirm results on this suppression from small-scale cosmic shear studies, while we exclude some hydro-dynamical simulations with too strong and too weak baryonic feedback. Our empirical prediction of the power spectrum suppression shows that studies of galaxy groups and clusters will be instrumental in unlocking the cosmological constraining power of future cosmic shear experiments like Euclid and Rubin-LSST, and invites further investigation of the baryon correction models.
Effect of variable crustal density on the surface magnetic field of radio pulsars
Authors: Sellick Kathleen , Ray Subharthi 
Published: January 2024

ABSTRACT
We study the surface magnetic field fluctuations due to radial oscillations as a viable cause for the microstructures of the radio pulsar pulse patterns. The electrical conductivity of matter in the outer layer of the crust of a neutron star (NS) plays a crucial role in the resulting surface magnetic field if we assume that the magnetic field is confined to this layer. This outer layer has a rapidly varying matter density that changes the microphysics of the material, affecting the electrical conductivity at every stage of the density change. In this study, the varying electrical conductivity in this rapidly varying density regime of the outer layer of the NS crust – from ∼1011 to about 104 g cm−3 – has been used to calculate the surface magnetic field using the induction equation. A finite effect of the strong gravitational field at the NS surface has also been taken into account. The equations have been solved in MATLAB using the method of lines. Any minor radial fluctuation due to stellar oscillation, in particular the radial oscillations, leads to a fluctuation of the electrical conductivity in the outer layer of the crust. This leads to fluctuations in the surface magnetic field with a frequency equal to the frequency of the stellar oscillation. We find that not only is the variation of the surface magnetic field substantial, but also it does not remain constant throughout the lifetime of the NS.