18 articles · 3 topics
Critical care medicine6 articles
Score 0.88 · ⓘ Reference
Frédéric Pène et al. — Intensive Care Medicine · August 19, 2026
Why it’s here: This randomized controlled trial directly addresses early red blood cell transfusion in septic shock, a core topic of interest.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.84 · ⓘ Reference
Guillaume Millot et al. — Intensive Care Medicine · August 20, 2026
Why it’s here: This randomized trial investigates antibiotic therapy for ventilator-associated pneumonia in mechanically ventilated patients, directly relevant to the researcher's interests.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.82 · ⚠ Check
Zhenyue Gao et al. — npj Digital Medicine · August 20, 2026
Why it’s here: This article directly addresses the development of a digital biomarker for illness severity in older ICU patients, aligning perfectly with the researcher's interest in ICU outcomes and clinical management.
⚠ a section is not grounded in the abstract (The 'Why It Matters' section contains unsupported claims. The sentence 'While the model shows strong performance and generalizability, the abstract does not detail specific limitations beyond the need for development and validation in this population.' is not supported by the abstract. The abstract does not mention specific limitations beyond the need for development and validation in this population; it states that 'interpretability and cross-institutional generalizability' were limited in prior work, and that their model addresses this. The abstract does not discuss the generalizability of the developed model in detail, nor does it mention specific limitations of the developed model.)
Study Design
This international multicenter study developed an interpretable time-series deep learning model, Elder-DDB, to create a dynamic digital biomarker for illness severity in critically ill older adults. The model was trained on over 41,000 elderly ICU admissions from more than 200 hospitals across the US, Europe, and Asia. External validation was performed on nearly 47,500 admissions from independent cohorts.
Key Results
The Elder-DDB model demonstrated robust and consistent discrimination across all external validation cohorts, with an AUROC range of 0.801–0.834. This performance significantly outperformed both conventional clinical scores and state-of-the-art deep learning baselines. Interpretability analyses confirmed the biomarker's reliable assessment of real-time illness severity.
Why It Matters
The developed dynamic digital biomarker offers a promising solution for continuous, individualized assessment of illness severity in elderly ICU patients, supporting precision critical care. While the model shows strong performance and generalizability, the abstract does not detail specific limitations beyond the need for development and validation in this population.
● Preprint · Score 0.70 · ✓ Verified
Champeaux, S. A. et al. — medRxiv Preprint · health informatics · August 21, 2026
Why it’s here: This article directly addresses sepsis prediction in the ICU using machine learning and discusses its translation to different ICU environments.
Study Design
This study evaluated a neonatal sepsis prediction framework by applying it to mixed intensive care unit (ICU) data from Great Ormond Street Hospital (GOSH) and then retraining the models locally. The methods involved harmonizing features and temporal sampling to align with a previously established pipeline from the Children's Hospital of Philadelphia (CHOP). Seven classifiers were initially evaluated using CHOP-trained weights before local retraining.
Key Results
Direct transfer of CHOP-trained models to the GOSH mixed ICU population resulted in reduced performance due to domain and population shift. However, retraining on GOSH data restored high discrimination, with Gradient Boosting achieving an AUC of 0.86 (vs. 0.87 at CHOP) and KNN achieving an AUC of 0.80 (vs. 0.79 at CHOP). Statistically significant performance gains were confirmed across all classifiers after local retraining (p < 0.001).
Why It Matters
The findings demonstrate that while direct transfer of clinical prediction models across different ICU settings is limited by domain shift, local retraining can effectively restore high discrimination. This highlights local adaptation as a practical strategy for the safe and generalizable deployment of prediction models in diverse clinical environments. Elements of the original preprocessing pipeline could not be reproduced, which further constrained transportability.
● Preprint · Score 0.70 · ✓ Verified
Discordant Evidence on Corticosteroids in Sepsis: A Meta-Research Study
Weibel, S. et al. — medRxiv Preprint · intensive care and critical care medicine · August 21, 2026
Why it’s here: This article is a meta-research study on discordant evidence regarding corticosteroids in sepsis, directly addressing a critical care topic and the quality of evidence from RCTs.
Study Design
This meta-research study examined 42 systematic reviews (SRs) published between 2015 and 2025 that evaluated corticosteroids for sepsis. The study assessed study-pool overlap using an SRxRCT inclusion matrix and Jaccard similarity, and classified SRs and RCTs by PICO profiles. Discordance in short-term mortality conclusions among clinically comparable SRs was explored.
Key Results
Among 38 SRs with short-term mortality meta-analyses, 15 (39%) reported benefit and 23 (61%) reported no evidence of effect. Discordance was observed exclusively among SRs evaluating broad corticosteroid strategies, while conclusions were consistent for specific regimens like hydrocortisone plus fludrocortisone (benefit) and hydrocortisone, ascorbic acid, and thiamine (no evidence of effect). SRs including sepsis +/- shock populations more frequently reported benefit (62%) than those restricted to septic shock (22%).
Why It Matters
Systematic reviews addressing similar clinical questions often synthesize different evidence bases and report discordant conclusions, complicating guideline development. Guideline developers should consider the underlying RCTs included in an SR to ensure they adequately represent the intended clinical question, beyond just methodological quality and reported PICO. Clinically coherent evidence syntheses are needed to improve the interpretability of pooled treatment effects and support more targeted corticosteroid therapy in sepsis.
✦ Something Different · Score 0.42 · ✓ Verified
Laura Jurema dos Santos et al. — Critical Care Medicine · August 21, 2026
✦ A change of pace: This article offers a unique perspective on the critical care research landscape itself, focusing on gender disparity in authorship, which is a departure from the clinical and technical topics typically found in the field.
Study Design
This cross-sectional bibliometric analysis examined original research publications in the ten critical care journals with the highest impact factors from 2018 to 2022. The study included 4,982 publications, comprising 50,357 authors, and categorized them by country income classification (HICs vs. L/MICs). Authorship proportions, including first and senior positions, were evaluated.
Key Results
Women accounted for 31.3% of all authors, 35.4% of first authors, and 21.4% of senior authors, with women's representation being higher in L/MICs (38.2%) compared to HICs (30.6%). Publications from L/MICs also showed a higher proportion of women in first (40.7% vs. 34.7%) and senior (27.6% vs. 20.7%) positions. Temporal increases in women's authorship were observed over time in both HICs and L/MICs.
Why It Matters
Substantial gender disparities in authorship persist within high-impact critical care journals, although women are better represented in publications originating from L/MICs. There was an observed increase in women's authorship over the study period from 2018 to 2022. Coauthorships involving at least one woman accounted for less than 30% across all publications.
Healthcare machine learning6 articles
★ Flagship · Score 0.83 · ⓘ Reference
Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial
Xiaoming Zhang et al. — Nature Medicine · August 19, 2026
Why it’s here: This article describes large-scale AI-guided liver malignancy diagnosis, including multicenter study and a single-arm trial, indicating real-world application.
Abstract unavailable — listed as a pointer; not summarized.
Score 0.82 · ✓ Verified
Jinze Zhang et al. — npj Digital Medicine · August 19, 2026
Why it’s here: This article directly addresses foundation models for end-to-end diagnostic workflow automation in clinical care, including prospective validation and real-world deployment.
Study Design
This study presents the Full-process OCT-based Clinical Utility System (FOCUS), a foundation model-driven framework for end-to-end automation of 3D OCT retinal disease diagnosis. The system sequentially performs image quality assessment, abnormality detection, and multi-disease classification, integrating 2D slice-level predictions into 3D patient-level diagnoses using a unified adaptive aggregation method.
Key Results
Trained on 3300 patients and validated on 1345 patients across diverse settings, FOCUS achieved high F1-scores for quality assessment (99.01%), abnormality detection (97.46%), and patient-level diagnosis (94.39%). Real-world validation showed stable performance (F1: 90.22–95.24%), and in human-machine comparisons, FOCUS matched expert performance in abnormality detection (F1: 95.47% vs 90.91%) and multi-disease diagnosis (F1: 93.49% vs 91.35%).
Why It Matters
FOCUS automates the image-to-diagnosis pipeline for 3D OCT retinal disease diagnosis, representing a critical advance towards unmanned ophthalmology and enhancing population-scale retinal care accessibility and efficiency. This work provides a validated blueprint for autonomous screening to improve care.
Score 0.76 · ✓ Verified
Ruowen He et al. — Critical Care · August 20, 2026
Why it’s here: This study develops and validates age-specific predictive models for sepsis in critically ill children using machine learning (CatBoost) and discusses interpretability, directly matching the researcher's interests.
Study Design
This multicenter study developed age-specific predictive models for sepsis onset in critically ill neonates and children. It utilized a large-scale dataset from the CALM2302 project, encompassing 17,066 patients across 30 Chinese hospitals between October 2020 and October 2023. CatBoost models were developed for NICU and PICU populations using approximately 200 hematological parameters.
Key Results
Simplified 18-variable models demonstrated robust performance, achieving internal AUCs of 0.846 for NICU and 0.803 for PICU, with external AUCs ranging from 0.717 to 0.827. These simplified models maintained highly comparable discriminative power to the full models. SHAP analysis identified 18 key predictive features for each cohort, with most being research parameters rather than traditional blood count indices.
Why It Matters
These findings offer a robust, interpretable, and clinically accessible solution for proactive sepsis management in intensive care units by capturing early hematological morphological alterations beyond traditional blood counts. The developed simplified models accurately predict sepsis in both neonatal and pediatric populations. Most identified key predictive features remained statistically significant after adjustment for age, sex, and study sites.
Score 0.76 · ✓ Verified
Fine-tuning an ECG foundation model to predict coronary CT angiography outcomes
Yujie Xiao et al. — npj Digital Medicine · August 18, 2026
Why it’s here: This article fine-tunes an ECG foundation model to predict coronary CT angiography outcomes, directly aligning with the interest in foundation models and predictive models in clinical care.
Study Design
This study developed an interpretable AI-ECG model to predict severe or complete stenosis of the four major coronary arteries on CCTA. The model was validated on both internal and external datasets, with performance assessed using AUCs for each coronary artery.
Key Results
On the internal validation set, the model achieved AUCs of 0.794 (RCA), 0.818 (LM), 0.744 (LAD), and 0.755 (LCX), while the external validation set showed AUCs of 0.749 (RCA), 0.971 (LM), 0.667 (LAD), and 0.727 (LCX). The model demonstrated stable performance in a clinically normal-ECG subset and across demographic and acquisition-time strata.
Why It Matters
This AI-ECG model offers a promising alternative for CAD screening, potentially overcoming limitations of CCTA such as equipment dependence and radiation exposure. Interpretability analyses revealed distinct waveform differences and highlighted key electrophysiological regions, providing new insights into ECG correlates of coronary stenosis.
★ Flagship · Score 0.75 · ⓘ Reference
Liron Leibovitch et al. — Nature Medicine · August 19, 2026
Why it’s here: This article directly addresses prospective evaluation of an LLM clinical decision support system in an emergency department, hitting multiple keywords of interest.
Abstract unavailable — listed as a pointer; not summarized.
✦ Something Different · Score 0.66 · ✓ Verified
І. А. Лурін et al. — Critical Care · August 23, 2026
✦ A change of pace: This article explores bacteriological profiling and antimicrobial resistance in combat-related infections, offering a unique and critical perspective distinct from typical clinical AI applications.
Study Design
This retrospective analysis examined 203 patients with ballistic injuries treated at a Role 3 military hospital in Ukraine between 2024 and 2025, yielding 685 unique strains. Isolates were categorized by year, sampling timing, and evacuation interval, with susceptibility testing following EUCAST v.14–15 and bacteriological profiling for MDR, XDR, and PDR microbes.
Key Results
Gram-negative bacteria predominated, with high carbapenem resistance observed in K. pneumoniae (imipenem 83.1%) and A. baumannii (imipenem 91.5%), while colistin retained activity. Patient-corrected MDR prevalence escalated from 72.9% in 2024 to 82.3% in 2025 (p = 0.022), and resistance was highest in early-evacuation/early-sampling (73.6% MDR) and late-evacuation/late-sampling (97.1% MDR) groups.
Why It Matters
Combat wounds present a severe burden of resistant Gram-negative pathogens, with chronological resistance plateaus linked to evacuation and sampling timelines, indicating a pervasive institutional reservoir across the evacuation network. A Random Forest model reliably predicted resistance (AUC = 0.718) in high-volume cohorts, suggesting optimized empirical selection, but early management should prioritize surgical debridement over premature antimicrobial escalation.
Hospital logistics and operations6 articles
Score 0.78 · ✓ Verified
Fair online hospital diagnostic service scheduling: Helping both patients and providers
Maureen Canellas et al. — Production and Operations Management · August 21, 2026
Why it’s here: This article directly addresses hospital diagnostic service scheduling, workload balancing, and patient flow, which are core to hospital operations and logistics.
Study Design
This study proposes a framework and develops an online algorithm, Patient–Provider Load-Balance (PPLB), to address workload imbalances in hospital diagnostic services. The research was conducted in collaboration with a large academic medical center in the Northeastern US, utilizing echocardiogram order data. Key factors analyzed include total workload, load difficulty, perception of load fairness, and workload variability.
Key Results
The PPLB algorithm effectively mitigates workload imbalance contributors, improving load fairness for technologists by up to 46% and limiting difficult scan loads. It also reduces daily workload variability compared to current hospital policy. The algorithm's performance was analyzed using echocardiogram order data, demonstrating its effectiveness.
Why It Matters
This work offers a novel algorithmic solution for fair online scheduling in diagnostic services, balancing technologist workload with patient and hospital needs. The PPLB algorithm improves patient wait times and throughput, particularly in high demand scenarios, without compromising operational performance. It demonstrates superior performance across both patient and provider factors compared to several benchmarks.
Score 0.74 · ✓ Verified
EXPRESS: Impact of Patient Navigation Programs on Emergency Department Utilization
Jiajia Qu, Raj Sharman, Indranil R. Bardhan — Production and Operations Management · August 18, 2026
Why it’s here: This article directly addresses patient navigation programs and their impact on emergency department utilization, a key aspect of hospital operations and patient flow.
Study Design
This study examines the impact of patient enrollment in a patient navigation (PN) program on emergency department (ED) utilization within a large Accountable Care Organization (ACO). The researchers employed a staggered difference-in-differences design combined with sliding-window, propensity score matching using longitudinal, patient-level data. Qualitative insights from healthcare practitioners were also incorporated to understand underlying mechanisms.
Key Results
PN enrollment was found to significantly reduce ED visit utilization, although this effect diminishes over time, indicating decreasing marginal returns to prolonged PN engagement. While reductions in ED utilization generate measurable cost savings, these savings alone do not fully offset per-patient program costs.
Why It Matters
This research contributes to operations management by revealing the dynamic effectiveness of care coordination interventions over time and across patient populations, offering insights for designing more targeted and cost-effective PN programs. The findings suggest that the full value of PN programs extends beyond ED utilization reductions to other aspects of patient care and care coordination. This study is among the first to examine the operational value of PN programs within a value-based care reimbursement setting.
★ Flagship · Score 0.71 · ✓ Verified
Run Zhang et al. — Manufacturing & Service Operations Management · August 19, 2026
Why it’s here: This article directly addresses optimizing emergency drone deployment and disposable defibrillator allocation for out-of-hospital cardiac arrest cases, using operations research models, which is highly relevant to emergency medical services logistics and operations.
Study Design
This study addresses the critical need for rapid medical response in out-of-hospital cardiac arrest (OHCA) cases by optimizing the strategic deployment of drones and disposable Automated External Defibrillators (AEDs). The problem is framed as a Modular Capacitated Maximal Covering Location Problem (MC-MCLP) that incorporates a time constraint for AED delivery within a narrow therapeutic window, using incomplete OHCA data and a distributionally robust optimization approach for demand variability.
Key Results
The model effectively maximizes the number of timely AED deliveries within a critical window, demonstrating significant increases in the number of patients reached within the critical time period in a case study of OHCA incidents in Virginia Beach. Extensive testing revealed the impact of key parameters on the model, highlighting trade-offs between operational efficiency and both reliability and fairness.
Why It Matters
This framework ensures prompt assistance to OHCA cases within the vital intervention window, promoting equitable resource allocation and enhancing OHCA survival rates by optimizing EMS resource distribution. It addresses primary challenges in EMS planning by improving response times within the crucial timeframe and establishing backup emergency resources.
★ Flagship · Score 0.67 · ✓ Verified
Omnichannel Operations in On-Demand Delivery Platform with Buy-Online-and-Pick-up-in-Store
Yu Guo et al. — Manufacturing & Service Operations Management · August 19, 2026
Why it’s here: Studies omnichannel operations and pricing strategies in on-demand delivery, with direct relevance to logistics, supply chain, and operational models.
Study Design
This paper studies an emerging omnichannel on-demand economy using a stylized model to analyze a platform's optimal pricing and wage-setting strategies. The model is then estimated using data from a leading meal-delivery platform in China to quantify the effects of buy-online-and-pick-up-in-store (BOPS) and wage regulations.
Key Results
The study finds that in an omnichannel environment with BOPS, price is not always positively related to demand for gig services, and the BOPS-channel price may not be lower than the delivery-channel price. While BOPS improves platform and merchant profits, it enhances consumer surplus only when market demand is sufficiently great and consistently reduces courier welfare.
Why It Matters
These findings suggest that traditional gig economy pricing strategies may be suboptimal in an omnichannel setting, and that BOPS adoption imposes welfare losses on couriers. Platform managers and policymakers should consider these uneven welfare distributions beyond aggregate gains.
Score 0.62 · ✓ Verified
Renate F. Wit, Art van Schaaijk, Ronald S. Batenburg — BMC Health Services Research · August 24, 2026
Why it’s here: This article directly addresses the nursing workforce shortage, projecting supply and demand and identifying strategies to mitigate the problem, which is a key aspect of hospital staffing and operations.
Study Design
This study utilized prognostic data from the Dutch Continuous Monitor of the Healthcare and Welfare Labour Market (AZW) to project future supply and demand for registered nurses (RNs) in the Netherlands. The AZW forecasting model was employed to conduct what-if analyses, examining the gap between supply and demand by adjusting factors like working hours, absenteeism, and entry/exit rates.
Key Results
Projections indicate a growing nursing shortage in the Netherlands, increasing from 5.7% of demand in 2023 to 14.6% by 2033. The most effective strategy assessed, combining a 20% reduction in nurse exit rates with a 1.5-hour increase in weekly working hours, could reduce the shortage to 1.4% by 2033.
Why It Matters
These findings highlight that retaining nurses is the most impactful strategy for addressing the supply-demand gap, suggesting a need to prioritize retention alongside recruitment efforts. While increasing working hours and reducing healthcare demand are also considered, the latter presents a more complex challenge.
● Preprint · ✦ Something Different · Score 0.46 · ✓ Verified
Hauser, K. A. et al. — medRxiv Preprint · emergency medicine · August 22, 2026
✦ A change of pace: This article offers a novel perspective on emergency care by focusing on a specific patient population (persons living with dementia) and age-friendly approaches, diverging from the more general operational and scheduling topics previously considered.
Study Design
This study describes the adoption and implementation of pragmatic Geriatric Emergency Department (GED) models of care in three San Francisco health system emergency departments. These programs aim to support and improve care for emergency department (ED) patients at risk for or living with dementia. Methods involved using screening and assessment tools to identify cognitive impairment and capture data for post-discharge care.
Key Results
Three San Francisco hospitals, including a tertiary care, critical access, and large integrated health system-community ED, independently implemented GED programs. Each program utilizes screening and assessment tools to identify patients at risk for cognitive impairment and captures data to facilitate post-discharge care. While all programs aim to support older adults, they varied in target patient population age and staff/resource allocation.
Why It Matters
Developing and disseminating GED care models is possible and sustainable for patients at risk of or living with dementia when aligned with health system goals through persistent value demonstration and communication. These interventions are designed to address geriatric syndromes, including dementia care, through continuous quality improvement. The findings highlight the feasibility of implementing such programs across different ED settings.
Curated from OpenAlex, Crossref & medRxiv. Summaries are machine-generated and grounded in each article’s abstract or open-access text; ⚠ flags a summary that needs a second look, and ✦ marks a deliberate change-of-pace pick.
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