18 articles · 3 topics
Critical care medicine6 articles
Score 0.90 · ✓ Verified
Chieh‐Ching Yen, Kuang-Yu Niu, Shang-Jun ZhangJian — Critical Care Medicine · August 5, 2026
Why it’s here: A systematic review on the prognostic accuracy of sepsis criteria in children, directly relevant to sepsis management and ICU outcomes.
Study Design
This systematic review and meta-analysis evaluated the prognostic accuracy of the Phoenix Sepsis Criteria (PSC) for in-hospital mortality in children younger than 18 years with suspected infection. Data were extracted from 15 studies comprising 16 cohorts and 2,601,038 encounters, with quality assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.
Key Results
The PSC demonstrated a pooled sensitivity of 0.77 and specificity of 0.73, with an area under the curve (AUC) of 0.81, indicating good prognostic accuracy for mortality. The PSC outperformed the International Pediatric Sepsis Consensus Conference (IPSCC) criteria, which had an AUC of 0.71.
Why It Matters
The PSC shows good prognostic accuracy for mortality and is superior to the IPSCC criteria, though its performance varies by setting, with lower specificity in the ICU compared to the ED. The PSC is intended for prognostic risk stratification, not frontline screening, and further validation in various settings is warranted.
Score 0.88 · ✓ Verified
Felipe González-Seguel et al. — Critical Care Medicine · August 7, 2026
Why it’s here: This systematic review and meta-analysis directly addresses muscle wasting in critically ill adults, a key ICU outcome.
Study Design
This systematic review and meta-analysis aimed to identify biological findings underlying skeletal muscle dysfunction in adults with critical illness. The study included 75 original studies, encompassing 2,023 patients and 642 controls, with data extracted from skeletal muscle biopsies.
Key Results
Myofiber cross-sectional area was significantly lower in critically ill patients by 22% before ICU discharge (MD, -689 µm2; p = 0.02) and after (MD, -775 µm2; p = 0.04). While muscle protein synthesis showed no significant difference compared to controls (MD, 0.007%/hr; p = 0.36), protein degradation pathway markers were consistently higher (standardized MD ranging, 0.5-1.7).
Why It Matters
Muscle wasting in critical illness is characterized by reduced myofiber size and increased protein degradation, despite normal protein synthesis rates. These findings underscore the complex biological disturbances contributing to muscle dysfunction in this population.
Score 0.82 · ✓ Verified
Caroline Neumann et al. — Critical Care Medicine · August 3, 2026
Why it’s here: This article investigates biomarkers for renal replacement therapy and mortality in sepsis and septic shock, directly relevant to ICU outcomes and sepsis management.
Study Design
This study performed a secondary analysis of prospectively collected samples from the large multicenter SISPCT randomized control trial. It included 971 patients with sepsis and septic shock from 33 ICUs in Germany, with a 90-day follow-up period. The researchers evaluated associations between H3.1 nucleosome levels and mortality and time to renal replacement therapy (RRT) using multivariable Cox regression.
Key Results
Admission H3.1 nucleosome levels were significantly higher in patients with septic shock compared to sepsis (median 921.84 vs. 432.71 ng/mL). Higher H3.1 levels were associated with increased 28-day mortality, with a one-unit increase in log10 H3.1 levels correlating to a 48% increase in mortality (aHR, 1.48). Furthermore, H3.1 levels were higher in patients requiring RRT for stage 3 AKI with septic shock versus sepsis (1832 vs. 801.4 ng/mL), and one log10 increase in H3.1 increased the risk of 28-day RRT by 80% (aHR, 1.8).
Why It Matters
Elevated H3.1 nucleosome levels at admission are associated with both increased 28-day mortality and the need for renal replacement therapy in patients with sepsis and septic shock. These findings suggest that H3.1 nucleosomes could serve as predictive biomarkers for these adverse outcomes. The study's analysis was based on a large, multicenter trial, providing robust data for these associations.
Score 0.72 · ✓ Verified
Vancomycin optimised dosing regimens for critically ill patients receiving renal replacement therapy
Marta Ulldemolins et al. — Critical Care · August 4, 2026
Why it’s here: Focuses on optimizing vancomycin dosing in critically ill patients on renal replacement therapy, highly relevant to clinical management.
Study Design
This prospective, international pharmacokinetic study aimed to develop individualised vancomycin dosing recommendations for critically ill patients on renal replacement therapy (RRT). A population pharmacokinetic model was developed and validated using pre- and post-filter plasma and effluent concentrations from 65 patients across various RRT modalities.
Key Results
Monte Carlo simulations revealed that vancomycin dosing requirements were significantly influenced by actual body weight, RRT intensity, and duration (p < 0.05). The study calculated the probability of achieving efficacy targets (AUC0-24h/MIC ≥ 400) while avoiding toxicity (AUC0-24h ≥ 700 mg.h/L) for different dosing regimens.
Why It Matters
An optimised dosing nomogram was developed, considering these clinical characteristics, to guide efficacious and non-toxic vancomycin dosing in critically ill patients receiving RRT. This nomogram addresses the uncertainty in vancomycin dosing due to high pharmacokinetic variability in this patient population.
Score 0.70 · ✓ Verified
Nicolas Fage et al. — Critical Care Medicine · August 3, 2026
Why it’s here: This article directly addresses hemodynamic support (MAP targets) and vasopressor use in septic shock within an ICU setting, including a randomized study design.
Study Design
This prospective physiologic and randomized study was conducted in the medical ICU of a university hospital. It included patients in the early phase of septic shock without chronic kidney disease or immediate need for renal replacement therapy. Patients underwent a standardized 2-hour MAP test with RRI measured at different MAP levels.
Key Results
Of 80 randomized patients, 23 (29%) were RRI responders, defined by an RRI decrease of at least 0.05 during the MAP test. Among RRI responders, a higher MAP target did not improve renal outcomes. RRI nonresponders showed higher urine output with a high MAP target, though other renal parameters did not differ significantly.
Why It Matters
A decrease in RRI during a MAP test did not identify patients who would benefit from higher MAP targets for improved renal function in early septic shock. However, the absence of an RRI decrease may suggest patients who experience increased urine output with higher MAP targets, though these findings are considered exploratory. The study highlights the lack of a validated bedside tool for individualized MAP targets.
● Preprint · ✦ Something Different · Score 0.66 · ✓ Verified
Ambese, T. Y., Dulla, P. K., Belachew, F. K. — medRxiv Preprint · intensive care and critical care medicine · August 3, 2026
✦ A change of pace: This article explores critical care challenges and implementation barriers in a low-resource setting, offering a perspective distinct from the highly technical and resource-intensive studies typically found in critical care medicine literature.
Study Design
This prospective, multicenter, mixed-methods study assessed critical illness burden and management in 12 public hospitals in Ethiopia. A quantitative point prevalence survey identified critically ill adult inpatients using vital sign criteria, followed for 7-day mortality, and evaluated hospital EECC resource availability. A qualitative component explored implementation barriers through key informant interviews and focus group discussions.
Key Results
Of 1,077 hospitalized patients, 221 (20.5%) were critically ill, with most (62.0%) managed in general wards and a 7-day mortality of 14.0% compared to 2.8% in non-critically ill patients. Only 1.8% of critically ill patients received all indicated EECC treatments, and median hospital EECC resource availability was 70.9%, with no hospital fully equipped.
Why It Matters
The study reveals a substantial burden of critical illness in Ethiopian hospitals, predominantly managed outside ICUs with profound gaps in basic EECC delivery and high short-term mortality. Qualitative findings identified interconnected barriers, underscoring the urgent need for decentralized EECC strengthening through training, resources, protocols, and addressing systemic issues to improve outcomes in resource-limited settings.
Healthcare machine learning6 articles
★ Flagship · Score 0.83 · ✓ Verified
Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people
Xuhai Xu et al. — Nature Medicine · August 4, 2026
Why it’s here: This article investigates the impact of explainable AI and LLMs on diagnostic performance in dermatology, covering AI in clinical care and LLMs.
Study Design
This study employed two large-scale experiments to investigate the influence of explainable AI (XAI) on dermatological diagnostic performance. The experiments involved 623 lay people and 153 primary care physicians (PCPs) interacting with a fairness-based AI model for dermatological diagnoses combined with different XAI-based explanations, particularly multimodal large language models (LLMs). The core method examined how XAI assistance impacts diagnostic accuracy and fairness across skin tones.
Key Results
Fairness-constrained AI training improved diagnostic accuracy and reduced skin-tone disparities for both lay people and PCPs. However, LLM explanations had divergent effects: lay users exhibited higher automation bias, with accuracy boosted by correct AI diagnoses but reduced by errors, while PCPs remained resilient, benefiting regardless of AI accuracy. Presenting the AI diagnosis before human decision-making also appeared to strengthen anchoring bias.
Why It Matters
These findings demonstrate that XAI, specifically LLMs, can act as a 'double-edged sword' in medical AI, with impacts varying significantly based on user expertise and the timing of AI prediction delivery. This highlights the need for careful design of human-AI collaborative systems to mitigate potential biases and optimize diagnostic performance. The study underscores the importance of considering user expertise and presentation timing when integrating AI into clinical workflows.
Score 0.82 · ✓ Verified
Meiwei Zhang et al. — npj Digital Medicine · August 8, 2026
Why it’s here: This article uses a multimodal large language model for a clinical triage tool, directly aligning with the researcher's interest in LLMs in medicine and predictive models for clinical care.
Study Design
This study developed a two-stage screening framework using a multimodal large language model (LLM) for osteoporotic vertebral compression fracture (OVCF) triage. Standardized posture images and functional videos from 204 participants (102 OVCF, 102 controls) were processed using pose estimation and prompt-guided LLM feature extraction.
Key Results
On external validation with 56 participants, the Gradient Boosting model achieved an AUC of 0.838, with 89.3% sensitivity and 71.4% specificity, showing negligible internal-to-external AUC degradation. Predicted probabilities were well-calibrated with a Brier score of 0.168, and post-hoc recalibration improved calibration.
Why It Matters
This framework enables accurate triage of symptomatic OVCF using home-captured posture and movement data, potentially prioritizing patients for confirmatory imaging in community and primary care settings. The LLM generated clinically coherent language, distinguishing OVCF from control narratives with high score-explanation concordance.
Score 0.78 · ✓ Verified
Intersectional fairness in vision-language models for medical image disease classification
Yupeng Zhang et al. — npj Digital Medicine · August 3, 2026
Why it’s here: This article presents a framework for fairness in vision-language models for medical image disease classification, directly relevant to AI in clinical decision support and bias mitigation.
Study Design
This study developed and evaluated a novel training framework called Cross-Modal Alignment Consistency (CMAC-MMD) to address intersectional biases in vision-language models (VLMs) for medical image disease classification. The approach was tested on 10,015 skin lesion images and 10,000 fundus images, with external validation on 12,000 skin lesion images, stratifying performance by age, gender, and race.
Key Results
In dermatology, CMAC-MMD reduced the intersectional missed diagnosis gap (ΔTPR) from 0.50 to 0.26 and improved AUC from 0.94 to 0.97. For glaucoma screening, the method decreased ΔTPR from 0.41 to 0.31 while achieving a higher AUC of 0.72 compared to the baseline 0.71.
Why It Matters
The findings provide a methodological foundation for developing clinical decision support systems that are accurate and equitable across diverse patient subgroups without compromising privacy during inference. This approach standardizes diagnostic certainty across intersectional patient groups, offering a potential solution to current fairness interventions that often fail or reduce overall performance.
Score 0.78 · ✓ Verified
Toward expert-level medical text validation with language models
Asad Aali et al. — npj Digital Medicine · August 4, 2026
Why it’s here: This research focuses on validating medical text generated by language models, directly addressing the deployment and safety of LLMs in clinical settings.
Study Design
This study introduces MedVAL, a novel, self-supervised, data-efficient distillation method designed to train evaluator language models (LMs) for assessing factual consistency in LM-generated medical outputs. The method leverages synthetic data and does not require physician labels or reference outputs, addressing challenges of cost and data availability in clinical settings. MedVAL-Bench, a dataset of 840 physician-annotated outputs across 6 clinical use cases, was developed to evaluate LM performance.
Key Results
MedVAL distillation significantly improves alignment with physicians across seen and unseen tasks, increasing average F1 scores from 66% to 83% (p < 0.001) for 10 state-of-the-art LMs. It improved the best-performing proprietary LM (GPT-4o) by 8% without physician-labeled data, demonstrating performance statistically non-inferior to a single human expert on a subset annotated by multiple physicians (p < 0.001).
Why It Matters
This work provides evidence of LMs approaching expert-level ability in risk-aware validation of LM-generated medical text, supporting a scalable pathway towards clinical integration. The open-sourcing of the codebase, MedVAL-Bench, and MedVAL-4B aims to facilitate further research and development in this critical area. The study highlights the potential for automated, efficient, and accurate validation of clinical AI outputs.
Score 0.72 · ✓ Verified
Duzhen Zhang et al. — npj Digital Medicine · August 4, 2026
Why it’s here: This article introduces a framework using Large Language Models (LLMs) to construct medical knowledge graphs, directly aligning with foundation models in medicine and AI research.
Study Design
This study introduces MedKGent, a Large Language Model (LLM) agent framework designed for constructing temporally evolving medical knowledge graphs. The framework utilizes over 10 million PubMed abstracts from 1975 to 2023, employing two specialized agents for knowledge extraction and graph construction.
Key Results
The resulting knowledge graph contains 156,275 entities and 2,971,384 triples, representing the largest LLM-derived medical KG to date. Automated and expert assessments demonstrated triple-validity rates approaching 90%, and downstream evaluations showed significant improvements in retrieval-augmented generation for LLMs.
Why It Matters
MedKGent provides a scalable and temporally aware infrastructure for medical knowledge representation and literature-grounded AI research. This framework addresses the limitations of current KG construction methods by incorporating the temporal dynamics of evolving medical knowledge.
★ Flagship · ✦ Something Different · Score 0.47 · ⓘ Reference
Francesco Branda, Massimo Ciccozzi — The Lancet Digital Health · August 1, 2026
✦ A change of pace: This article offers a critical perspective on AI in medicine by discussing a fabricated disease, which provides a unique and cautionary angle distinct from the more technical or application-focused articles.
Abstract unavailable — listed as a pointer; not summarized.
Hospital logistics and operations6 articles
★ Flagship · Score 0.77 · ✓ Verified
Philipp Afèche, Vahid Sarhangian — Management Science · August 6, 2026
Why it’s here: This article directly addresses queueing models, rational abandonment, and performance implications in service systems like emergency departments, aligning with the researcher's interest.
Study Design
This paper studies rational abandonment behavior in an observable two-class priority queue using a threshold-based last-come, first-abandon strategy. The analysis characterizes this strategy and examines steady-state system performance under rational abandonment compared to an exogenous abandonment model.
Key Results
The study reveals that using an exogenous abandonment model can lead to suboptimal capacity planning decisions regarding target waiting times and abandonment rates. For pricing, welfare maximization necessitates only a service fee, while revenue maximization typically requires both an entrance and a service fee.
Why It Matters
The findings underscore the critical role of payment timing in service systems, demonstrating that welfare-maximizing prices inherently involve abandonment, whereas revenue maximization might aim to prevent it. This has significant implications for applications like emergency departments, highlighting the risks of charging non-urgent patients upfront.
★ Flagship · Score 0.75 · ✓ Verified
Online Resource Allocation with Convex-Set Machine-Learned Advice
Negin Golrezaei, Patrick Jaillet, Zijie Zhou — Operations Research · August 6, 2026
Why it’s here: This article applies operations research and machine learning to online resource allocation, relevant to capacity and demand management.
Study Design
This paper introduces a new framework for real-time resource allocation that moves beyond single-point demand forecasts. The approach represents machine-learned forecasts as a convex uncertainty set, capturing a range of possible demand outcomes for different customer groups. Adaptive online algorithms with theoretical guarantees are developed to manage this richer form of advice.
Key Results
The proposed framework optimally balances performance when forecasts are accurate with robustness when forecasts are inaccurate or misleading. Numerical studies demonstrate that this approach outperforms methods relying solely on single-point demand predictions.
Why It Matters
This work provides a more robust method for real-time resource allocation by incorporating demand uncertainty and variability. By representing forecasts as convex sets, the framework allows realized demand to differ from predictions while still optimizing allocation decisions.
★ Flagship · Score 0.71 · ✓ Verified
Ruochen Wang et al. — Management Science · August 5, 2026
Why it’s here: This article uses queueing models and simulation to analyze organ transplantation logistics and patient prioritization.
Study Design
This study introduces targeted priority mechanisms inspired by the Eurotransplant Senior Program (ESP) to address organ supply-demand imbalance. Using a comprehensive queueing model, the research analyzes strategic decisions of waitlisted candidates under these voluntary mechanisms, which grant explicit priority to disadvantaged patients who restrict their offer acceptance. The optimal program design is established through equilibrium analysis.
Key Results
A case study within the U.S. kidney allocation system for elderly patients (aged 65+) revealed a significant participation rate of 70%, even for conventionally marginal kidneys. Simulation outcomes indicate potential increases of up to 920 transplants annually, a 25% decrease in nonuse, and prevention of up to 193 waitlist deaths compared to the current system.
Why It Matters
These findings demonstrate the potential of targeted priority mechanisms to improve overall social welfare and organ-recipient matching without adversely affecting any patient group, including nonelderly patients. The mechanisms incentivize efficient matching by offering voluntary participation and explicit priority, thereby addressing organ underutilization in a real-world context.
★ Flagship · Score 0.71 · ✓ Verified
Tongwen Wu, Zuo‐Jun Max Shen, Yanzhi Li — Manufacturing & Service Operations Management · August 5, 2026
Why it’s here: This article directly addresses order fulfillment, workload balancing, and operational costs in online retail using optimization models, highly relevant to logistics and operations management principles applicable to healthcare.
Study Design
This study investigates anticipatory packing, a strategy where packages are prepared during non-peak periods for subsequent peak periods in online retail fulfillment centers. The research develops a two-stage sample-average approximation model using recent order data to optimize package selection for this practice.
Key Results
The proposed methods achieve approximation ratios of [Formula: see text] and [Formula: see text] for orders with laminar structures, where [Formula: see text] is the maximum size of prepackages. Experimental results with real-world data indicate that anticipatory packing can reduce operational costs by over 7% and significantly lower fixed investment expenses.
Why It Matters
Anticipatory packing is presented as a potent analytics-driven operational strategy with the potential to substantially decrease both operating and fixed investment costs for online retailers. This approach addresses the challenge of imbalanced workloads and soaring operational costs caused by erratic order arrivals.
Score 0.68 · ✓ Verified
Morgan Price, Tlell Elviss, Elka Humphrys — BMC Health Services Research · August 7, 2026
Why it’s here: Directly addresses capacity estimation and workforce planning tools for primary care, highly relevant to healthcare operations and resource management.
Study Design
This paper describes the intervention development study for CapEs, a primary care CAPacity EStimator simulation software. Requirements were gathered through stakeholder interviews and a co-design workshop, with the simulation model built in Stella Architect 3 using published literature and evidence from seven modified Delphi studies. CapEs was implemented by embedding it in facilitated workshops with primary care planners.
Key Results
The simulation model predicts the percentage of primary care needs met by clinically diverse teams, even with limited local data. Forty-five plans or communities across three Canadian provinces have been reviewed using CapEs in facilitated workshops. Benchmarking against BC policy panel targets showed broadly consistent results with some differences indicating potential refinements to both the model and policy targets.
Why It Matters
Tools like CapEs that estimate capacity match offer a more nuanced view of primary care workforce adequacy compared to panel targets. Embedding these tools in facilitated workshops made them accessible to planners with varying knowledge and data availability. Further validation and population-level data are needed to strengthen these models for primary care workforce planning in Canada.
✦ Something Different · Score 0.46 · ✓ Verified
Loay Zaknoun, Shebly Tannous, Salman Zarka — BMC Health Services Research · August 7, 2026
✦ A change of pace: This article offers a unique perspective on hospital operations by examining safety under extreme conditions of armed conflict, a stark contrast to typical efficiency-focused research.
Study Design
This practice-informed institutional case study examined patient safety challenges and adaptive responses in a frontline civilian hospital in northern Israel. The analysis drew on institutional operational materials, including safety rounds, management reviews, and incident reports, using an inductive and iterative interpretive review.
Key Results
Four interrelated domains of safety challenges were identified: environmental and infrastructural conditions, clinical care processes, workforce and team dynamics, and patient flow and organizational adaptation. Recurrent transitions between routine and protected care areas disrupted established work environments, clinical routines, team configurations, and organizational interfaces.
Why It Matters
Routine inpatient safety during prolonged armed conflict is shaped by interacting environmental, clinical, workforce, and organizational conditions requiring recurrent adaptation and organizational re-stabilization. These findings may inform preparedness and resilience planning in hospitals operating under comparable conditions, though the study did not evaluate the effectiveness of specific adaptations or patient safety outcomes.
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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