bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review update
Authors: Parekh Archie , Satish Samarth , Dulhanty Louise , Berzuini Carlo , Patel Hiren 
Published: December 2023

Background
A systematic review of clinical prediction models for aneurysmal subarachnoid hemorrhage (aSAH) reported in 2011 noted that clinical prediction models for aSAH were developed using poor methods and were not externally validated. This study aimed to update the above review to guide the future development of predictive models in aSAH.

Methods
We systematically searched Embase and MEDLINE databases (January 2010 to February 2022) for articles that reported the development of a clinical prediction model to predict functional outcomes in aSAH. Our reviews are based on the items included in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) checklist, and on data abstracted from each study in accord with the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) 2014 checklist. Bias and applicability were assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST).

Results
We reviewed data on 30 466 patients contributing to 29 prediction models abstracted from 22 studies identified from an initial search of 7858 studies. Most models were developed using logistic regression (n=20) or machine learning (n=9) with prognostic variables selected through a range of methods. Age (n=13), World Federation of Neurological Surgeons (WFNS) grade (n=11), hypertension (n=6), aneurysm size (n=5), Fisher grade (n=12), Hunt and Hess score (n=5), and Glasgow Coma Scale (n=8) were the variables most frequently included in the reported models. External validation was performed in only four studies. All but one model had a high or unclear risk of bias due to poor performance or lack of validation.

Conclusion
Externally validated models for the prediction of functional outcome in aSAH patients have now become available. However, most of them still have a high risk of bias.
Comorbidities and chance of remission in patients with early rheumatoid arthritis receiving methotrexate as first-line therapy: a Swedish observational nationwide study
Authors: Tidblad Liselotte , Westerlind Helga , Delcoigne Bénédicte , Askling Johan , Saevarsdottir Saedis 
Published: December 2023

ObjectivesThis study aims to examine whether comorbidities affect the likelihood of reaching primary remission on methotrexate monotherapy as the first disease-modifying antirheumatic drug (DMARD) in early rheumatoid arthritis (RA).MethodsWe used nationwide Swedish clinical and quality registers to collect RA disease activity measures and comorbidity data for patients diagnosed with RA 2007–2020 (n=11 001). The primary outcome was failure to reach 28-joint Disease Activity Score (DAS28) remission at 3 months. Secondary outcomes included Boolean, Simplified Disease Activity Index/Clinical Disease Activity Index remission, European Alliance of Associations for Rheumatology response and no swollen joint count at 3 and 6 months. For each comorbidity, and for combinations thereof, we calculated adjusted relative risks (RRs) of failure to reach remission, using modified Poisson regression.ResultsIn total, 53% (n=4019/7643) failed to reach DAS28 remission after 3 months of methotrexate monotherapy, ranging from 66% (n=25/38) among patients with chronic kidney disease to 48% (n=154/319) in patients with previous cancer. The risk of not reaching DAS28 remission at 3 months (RR adjusted for sex and age) was increased among patients with endocrine (RR 1.08, 95% CI 1.01 to 1.15), gastrointestinal (RR 1.16, 95% CI 1.03 to 1.30), infectious (RR 1.21, 95% CI 1.06 to 1.38), psychiatric (RR 1.24, 95% CI 1.15 to 1.35) and respiratory comorbidities (RR 1.16, 95% CI 1.01 to 1.32). Having three or more comorbidity categories was associated with a 27% higher risk of DAS28 remission failure at 3 months. A similar pattern was observed for the secondary outcomes.ConclusionsComorbidities decrease the chance of reaching remission on methotrexate as DMARD monotherapy in patients with early RA and are important to consider when assessing treatment outcomes.
AI Explainability and Acceptance: A Case Study for Underwater Mine Hunting
Authors: Richard Guy-Junior , Habonneau Jérôme , Guériot Didier , Le Caillec Jean-Marc 
Published: December 2023

In critical operational context such as Mine Warfare, Automatic Target Recognition (ATR) algorithms are still hardly accepted. The complexity of their decision-making hampers understanding of predictions despite performances approaching human expert ones. Much research has been done in Explainability Artificial Intelligence (XAI) field to avoid this “black box” effect. This field of research attempts to provide explanations for the decision-making of complex networks to promote their acceptability. Most of the explanation methods applied on image classifier networks provide heat maps. These maps highlight pixels according to their importance in decision-making.
In this work, we first implement different XAI methods for the automatic classification of Synthetic Aperture Sonar (SAS) images by convolutional neural networks (CNN). These different methods are based on a post hoc approach. We study and compare the different heat maps obtained.
Second, we evaluate the benefits and the usefulness of explainability in an operational framework for collaboration. To do this, different user tests are carried out with different levels of assistance, ranging from classification for an unaided operator to classification with explained ATR. These tests allow us to study whether heat maps are useful in this context.
The results obtained show that the heat maps explanation has a disputed utility according to the operators. Heat map presence does not increase the quality of the classifications. On the contrary, it even increases the response time. Nevertheless, half of operators see a certain usefulness in heat maps explanation.
Trustworthy and Efficient Digital Twins in Post-Quantum Era with Hybrid Hardware-Assisted Signatures
Authors: Nouma Saif E. , Yavuz Attila A. 
Published: December 2023

Digital Twins (DT) virtually model cyber-physical objects via sensory inputs by simulating or monitoring their behavior. Therefore, DTs usually harbor vast quantities of Internet of Things (IoT) components (e.g., sensors) that gather, process, and offload sensitive information (e.g., healthcare) to the cloud. It is imperative to ensure the trustworthiness of such sensitive information with long-term and compromise-resilient security guarantees. Digital signatures provide scalable authentication and integrity with non-repudiation and are vital tools for DTs. Post-quantum cryptography (PQC) and forward-secure signatures are two fundamental tools to offer long-term security and breach resiliency. However, NIST-PQC signature standards are exorbitantly costly for embedded DT components and are infeasible when forward-security is also considered. Moreover, NIST-PQC signatures do not admit aggregation, which is a highly desirable feature to mitigate the heavy storage and transmission burden in DTs. Finally, NIST recommends hybrid PQ solutions to enable cryptographic agility and transitional security. Yet, there is a significant gap in the state of the art in the achievement of all these advanced features simultaneously. Therefore, there is a significant need for lightweight digital signatures that offer compromise resiliency and compactness while permitting transitional security into the PQ era for DTs.

We create a series of highly lightweight digital signatures called Hardware-ASisted Efficient Signature (
HASES
) that meets the above requirements. The core of
HASES
is a hardware-assisted cryptographic commitment construct oracle (
CCO
) that permits verifiers to obtain expensive commitments without signer interaction. We created three
HASES
schemes:
PQ-HASES
is a forward-secure PQ signature,
LA-HASES
is an efficient aggregate Elliptic-Curve signature, and
HY-HASES
is a novel hybrid scheme that combines
PQ-HASES
and
LA-HASES
with novel strong nesting and sequential aggregation.
HASES
does not require a secure-hardware on the signer. We prove that
HASES
schemes are secure and implemented them on commodity hardware and and 8-bit AVR ATmega2560. Our experiments confirm that
PQ-HASES
and
LA-HASES
are two magnitudes of times more signer efficient than their PQ and conventional-secure counterparts, respectively.
HY-HASES
outperforms NIST PQC and conventional signature combinations, offering a standard-compliant transitional solution for emerging DTs. We open-source
HASES
schemes for public-testing and adaptation.
Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression
Authors: Ligero Marta , Serna Garazi , El Nahhas Omar S.M. , Sansano Irene , Mauchanski Siarhei , Viaplana Cristina , Calderaro Julien , Toledo Rodrigo A. , Dienstmann Rodrigo , Vanguri Rami S. , Sauter Jennifer L. , Sanchez-Vega Francisco , Shah Sohrab P. , Ramón y Cajal Santiago , Garralda Elena , Nuciforo Paolo , Perez-Lopez Raquel , Kather Jakob Nikolas 
Published: December 2023

Abstract

Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1–stained slides from the non–small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1–2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96–2.2), P = 0.082] and CPS [HR: 1.2 (0.79–1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity.

Significance:
The weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.
Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure
Authors: Kaur Dhamanpreet , Hughes J. Weston , Rogers Albert J. , Kang Guson , Narayan Sanjiv M. , Ashley Euan A. , Perez Marco V. 
Published: December 2023

BACKGROUND:
Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied.

METHODS:
This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex.

RESULTS:
There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores.

CONCLUSIONS:
The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.
Belongingness & Sport : A theoretical framework to conceptualize belonging with athletes
Authors: Slaten Christopher , Klotthor Noah , Lewis Christopher , Lee Jisu 
Published: December 2023

Abstract
A sense of belonging or connection to others has been seen as a fundamental need by social scientists across time. This connection to others leads to positive outcomes in almost every domain and sport is no exception to this rule. In the world of team sports, connecting to others has a huge impact on both performance and satisfaction and is essential for success. The following paper will discuss: the roots of the concept of belonging; the application of belonging to sport; a potential framework for understanding individual and environmental factors of belonging; implications of the impact of belonging for athletes.
A Climate Extremes Resilience Index for the Conterminous United States
Authors: Narayanan Anuska , Peter Brad G. , Keellings David 
Published: December 2023

Abstract
In recent decades, changes in precipitation, temperature, and air circulation patterns have led to increases in the occurrences of extreme weather events. These events can have devastating effects on communities causing destruction to property and croplands, as well as negative impacts on public health. As changes in the climate are projected to continue throughout the remainder of the twenty-first century, the ability for a community to plan for extreme weather events is essential to its survival. In this paper, we introduce a new index for examining the potential impacts of climate extremes on community resilience throughout the conterminous United States at the county level. We use an established disaster resilience index (baseline resilience indicators for communities) together with a revised version of the U.S. climate extremes index to create a combined measure of climate resilience—the climate extremes resilience index (CERI). To demonstrate the index, we test it on the 2021 Pacific Northwest heat wave, a 1000-yr weather event made 150 times as likely by climate change. To promote the use of the index, we also introduce a Google Earth Engine web app to calculate and map the CERI for the CONUS. By developing a web application for calculating the CERI, we expand the use of climate-resilience indices beyond theoretical applications. We anticipate that this tool and the CERI could be useful for policy makers to plan for climate-related disasters, as well as help the public with understanding and visualizing the impacts of extreme climatic events.
Cornell Notes from Underground: Whiteness, Neoliberalism, and AVID for Higher Education
Authors: Lybeck Rick 
Published: December 2023

Context: This critical narrative study analyzes the discursive means by which the educational nonprofit AVID acculturates teacher educators and teacher preparation programs to its ideological network. Through scenes reconstructed from Cornell notes taken at an AVID Summer Institute, the author examines his double identity as an AVID for Higher Education (AHE) trainee, simultaneously alienated as a social justice educator but complicit in co-constructing AVID’s target white neoliberal identity. Purpose: This article aims to encourage resistant risk-taking among critically leaning educators who may find themselves either avoiding white neoliberal educational reform activities taking place in their programs or complying with them against their better judgment. By co-conspiring to carry out critical interventions during white neoliberal reform activities, or by conducting critical “underground” research as this article exemplifies, white neoliberal educational reform may be decentered and more space opened for anti-oppressive pedagogies in teacher education. Research Design: This study combines autobiographical and arts-informed methods from narrative inquiry to examine the double identity that emerged for the author during AHE training. Beginning with critical Cornell notes taken and kept “underground” during training, the author turns to Fyodor Dostoyevsky’s novella Notes From Underground as a guide to analysis, showing that critical resistance to the culture of positivism white neoliberalism promotes is both long-standing and not won by working alone. Recommendations: The article recommends that, in the struggle to center racial and social justice in teacher education, critical educators should co-conspire and actively seek out white neoliberal reform activities, prepared to intervene with knowledge not only from critical theories but from the histories of race that drive the discourses reform organizations use to acculturate teacher educators to their ideological networks.
Use and Impact of Simulation in Family Caregiver Education: A Systematic Review
Authors: Hur Yujin , Hickman Ronald L. 
Published: December 2023

Background: Involving family caregivers in a team-based approach has become increasingly important. Simulation-based interventions in nursing have been effective. However, the effect of simulation on family caregiver education is not widely known and is limited. Objective: This study aimed to describe the current state of simulation-based interventions in family caregivers of patients with chronic diseases. Methods: This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The literature search was conducted in PubMed, CINAHL, PsycINFO, and Cochrane with a medical librarian’s help. This review included quantitative, primary, peer-reviewed English-written research articles that reported outcomes for family caregivers. We focused on the data about purpose, design, setting, population, intervention characteristics, and outcomes. Before analyzing the selected studies, we evaluated the risk of bias using the revised Cochrane Risk-Of-Bias tool for randomized trials. Results: Our search yielded 9 articles that met the inclusion criteria. The majority of the 9 articles analyzed were designed in a quasi-experimental design. In addition, most studies focused on certain diseases and used low-fidelity simulators. The simulation content mainly focused on technical simulations to help care for patients. Some simulations had a positive influence on family caregivers’ knowledge, attitudes, and skills. Conclusions: We expect more simulation-based interventions for caregivers, especially those targeting diverse populations, using appropriate modalities and randomized control designs.
Changing Horizon in Teaching Profession in India: Issues and Challenges
Authors: Verma Shikha , Sachdeva Mitima , Bajpai Amita 
Published: December 2023

Background: The education system is the lifeline of a nation. If the education system is strong and free from issues, the nation will, certainly, progress. Teachers have a key role in nation-building. Their opinions for overcoming the issues and challenges of education in the changing horizon can work as a roadmap to quality education. Purpose: The researcher has surveyed 450 teaching professionals to explore the issues and challenges of the changing horizon for teaching professionals in India. Methods: The researcher has developed a five-point rating scale, after an extensive literature review of regulations and reports of commissions and regulatory bodies. MS Excel 2016 version is used and analysis has been done by finding the frequency of respondents on each point of the rating scale. Then, the results have been interpreted and concluded. Results: The recommendations to overcome the issues and challenges for teaching professionals are also given in the changing horizon in India. Conclusion: Concerned professionals must consider factors including adequate infrastructure, up-to-date curricula, qualified faculty, suitable class sizes, learning materials, financial support and well-planned guidelines for quality education in order to improve education quality. A new teacher education program named as Integrated Teacher Education Programme (ITEP)1 and a reform in educational policy as New Education Policy (2020)2 have begun. All the teaching professionals are hopefully waiting for their implementation. A great future for everyone will undoubtedly result from our work.
A bite of dark chocolate? Black humour in mental health services
Authors: Kim Aram , Luo Rosie , Ng Lillian 
Published: December 2023

Objective Black humour permits expression of what may otherwise be unspeakable and is observed and used by staff working in mental health services. The aim of this study was to identify how humour, particularly black humour, was perceived by different health professionals in psychiatric practice. Methods Participants were invited to complete a survey. Data was collated and statistically analysed by a biostatistician. Chi square and univariate tests were performed to identify associations between categories. Results The sub-question relating to the benefits of black humour was analysed. Main findings were that the majority of staff perceived black humour to be beneficial in psychiatric practice ( n = 564 of 710 total; 79.4%), particularly if they used general and black humour with patients, families and colleagues. Those who observed black humour being used collegially about patients and families were more likely to find black humour beneficial; and even those uncomfortable with black humour being used by colleagues were more likely to see the benefits of black humour. Conclusion Black humour was perceived to be beneficial in mental health settings when used mindfully, sensitively and in context. Further study with patients and relatives may shed light on how widely the perception of benefit is shared.