18 articles · 3 topics
Critical care medicine6 articles
Score 0.78 · ✓ Verified
Roberto Santa Cruz et al. — Critical Care Medicine · October 20, 2026
Why it’s here: Systematic review and meta-analysis on tidal volume in noninvasive ventilation, directly relevant to mechanical ventilation in critical care.
Study Design
This systematic review and meta-analysis evaluated the association between tidal volume (Vt) and clinical outcomes in adults with acute respiratory failure (ARF) treated with noninvasive ventilation (NIV). Data were sourced from MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science, and Latin American and Caribbean Health Science Literature, covering studies from 1976 to September 2025. Observational studies reporting Vt or Vt normalized to predicted body weight (Vt/PBW) in NIV success and failure groups were included.
Key Results
Higher Vt/PBW was associated with NIV failure (mean difference [MD], 1.02 mL/kg) and worse outcomes, particularly in hypoxemic ARF (MD, 0.89 mL/kg). Post hoc analyses indicated that higher Vt/PBW correlated with increased mortality (odds ratio, 9.11), a higher respiratory rate (MD, 3.14 breaths/min), and lower Paco2 (MD, -2.38 mm Hg). Absolute Vt did not differ between NIV success and failure groups.
Why It Matters
The findings suggest that higher Vt/PBW is a prognostic marker for NIV failure and adverse outcomes in ARF, but these associations do not establish causality. The certainty of evidence ranged from very low to moderate, highlighting the need for interventional studies with systematic Vt monitoring. Elevated Vt/PBW, higher respiratory rate, and lower Paco2, without differences in pressure support, collectively implicate elevated respiratory drive as a potential driver of increased mortality.
Score 0.78 · ✓ Verified
Simon Lønstrup et al. — Critical Care Medicine · October 21, 2026
Why it’s here: Examines randomization methods in prehospital, emergency, and critical care trials, highly relevant to research methodology in the field.
Study Design
This meta-epidemiological study assessed differences in effect estimates between cluster-randomized clinical trials (cRCTs) and individually randomized clinical trials (iRCTs) in prehospital, emergency, or critical care settings. Eligible meta-analyses were identified through a systematic literature search of PubMed and Embase, and data were extracted in duplicate. The primary outcome was mortality, with secondary outcomes including length-of-stay or days-alive.
Key Results
The analysis included 65 meta-analyses comprising 212 randomized trials (39 cRCTs, 173 iRCTs) and 315,276 patients. No statistically significant difference in absolute ln(odds ratio) for mortality was found between cRCTs and iRCTs (MD, -0.110; 95% CI, -0.093 to 0.313). However, a significant difference was observed for length-of-stay or days-alive outcomes (MD, -0.202; 95% CI, -0.392 to -0.012), with cRCTs showing smaller effect size magnitudes.
Why It Matters
A systematic difference favoring iRCTs was identified for length-of-stay or days-alive outcomes, but not for mortality, suggesting potential biases in cRCTs in these settings. The finding that less than half of identified meta-analyses reported randomization type underscores a need for improved transparency and statistical consideration of randomization modality in future research. These results highlight the importance of accounting for randomization type in meta-analyses within acute care research.
Score 0.76 · ⚠ Check
Arabella Fischer-Hammerschmied et al. — Critical Care · October 24, 2026
Why it’s here: This article directly investigates skeletal muscle recovery after critical illness in ICU survivors, aligning perfectly with ICU outcomes and clinical management.
⚠ a section is not grounded in the abstract (The sentence 'The study found that ICU survivors showed lower activation of the autophagy-lysosomal pathway at baseline.' in the 'Why It Matters' section is not directly and unambiguously supported by the abstract. While the abstract states 'At baseline, ICU survivors showed lower activation of the autophagy-lysosomal pathway,' this is presented as a result, not as a finding that matters for the 'Why It Matters' section's main point about post-intervention rates and rehabilitation support.)
Study Design
This study investigated the skeletal muscle anabolic response to resistance exercise and protein intake in intensive care unit (ICU) survivors compared to matched controls. Muscle biopsies were taken before and after the intervention, with muscle protein fractional synthetic rate measured via [ring-²H₅]phenylalanine incorporation.
Key Results
ICU survivors had lower muscle thickness and handgrip strength but a higher baseline muscle protein synthesis rate (0.041%/hour vs. 0.030%/hour, p = 0.009) than controls. While the increase in muscle protein synthesis was smaller in survivors (Δ 0.031 vs. Δ 0.040%/hour, p = 0.04), post-intervention rates were similar between groups (0.071%/hour vs. 0.070%/hour, p = 0.70).
Why It Matters
Despite a smaller increase in muscle protein synthesis, post-intervention rates were similar between groups, providing mechanistic support for structured exercise-nutrition rehabilitation to promote muscle recovery after critical illness. The study found that ICU survivors showed lower activation of the autophagy-lysosomal pathway at baseline.
● Preprint · Score 0.76 · ✓ Verified
Ding, H. et al. — medRxiv Preprint · intensive care and critical care medicine · October 19, 2026
Why it’s here: Focuses on organ dysfunction trajectories, immune profiles, and outcomes in severely injured trauma patients, directly aligning with critical care medicine interests.
Study Design
This prospective, longitudinal, observational cohort study investigated severely injured trauma patients at high risk for multiple organ dysfunction. Organ dysfunction was quantified daily through 28 days using a trauma-specific modified Sequential Organ Failure Assessment (mSOFA) score, excluding the neurologic subdomain. Group-based trajectory modeling was used to identify distinct longitudinal mSOFA phenotypes.
Key Results
Trajectory modeling identified five mSOFA phenotypes (c1-c5) that differed in resuscitation requirements, shock severity, and 28-day mortality, but not baseline demographics or injury severity. Forty of 92 measured immune markers showed phenotype-differentiated trajectories, with specific markers like IL-17C, CXCL5, and CXCL6 distinguishing the most fulminant phenotype (c5). The phenotype with the highest CCI incidence (c3) was characterized by rising CXCL9, ARTN, and TWEAK.
Why It Matters
Organ dysfunction after severe trauma exhibits greater biological heterogeneity than traditional classifications suggest, with distinct mSOFA trajectory phenotypes corresponding to unique immune signatures. These findings support separable pathobiology rather than a single axis of injury severity. This has implications for developing phenotype-targeted precision immunomodulatory approaches in critical illness.
Score 0.72 · ⚠ Check
Pierre Mora et al. — Critical Care Medicine · October 21, 2026
Why it’s here: Directly addresses septic shock and publication trends, highly relevant to critical care research.
⚠ a section is not grounded in the abstract (The 'Why It Matters' section states: 'Around 20% of authors could not be classified by the NamSor API, indicating a potential limitation in gender analysis.' While the abstract mentions that 'around 20% of authors not classifiable by the NamSor API', it does not explicitly state that this indicates 'a potential limitation in gender analysis'. This is an interpretation or conclusion drawn from the data, not a direct statement within the abstract.)
Study Design
This meta-epidemiological study systematically searched PubMed, Embase, and Web of Science for articles indexed with "septic shock" between January 2014 and December 2024. Peer-reviewed publication types were eligible, while conference abstracts and non-peer-reviewed material were excluded. Bibliographic data, author affiliations, country income level, and journal prestige were extracted, with author gender inferred using the NamSor API.
Key Results
Of 10,352 eligible publications, LMIC-affiliated first authors increased by 2.13% per year, rising from 18% in 2014 to 37% in 2024. Women represented 35.6% of first authors and 25.0% of last authors, with higher proportions in LMICs than HICs for both (44.5% vs. 32.5% for first authors, and 35.2% vs. 21.5% for last authors). Articles led by men in HICs were more often published in higher-SJR journals compared with those led by women, with no such gender differences observed among LMIC-affiliated authors.
Why It Matters
Despite encouraging growth in LMIC contributions and female representation, major inequities persist in scientific output on septic shock. Strengthening equitable collaborations and supporting underrepresented groups are crucial to broaden participation and enhance the global applicability of critical care research. Around 20% of authors could not be classified by the NamSor API, indicating a potential limitation in gender analysis.
✦ Something Different · Score 0.72 · ✓ Verified
Sheila Pereiro Martínez et al. — Critical Care Medicine · October 21, 2026
✦ A change of pace: This randomized controlled trial on music therapy during mechanical ventilation weaning offers a novel, non-pharmacological intervention that diverges significantly from the reader's typical focus on physiological and pharmacological treatments.
Study Design
This randomized controlled trial enrolled 100 adult critically ill patients receiving invasive mechanical ventilation for at least 48 hours in a general ICU. Patients were randomized to either standard care or standard care plus daily, individualized music therapy sessions averaging 25 minutes. The music therapy included relaxation, listening, and clinical improvisation based on patient needs and preferences.
Key Results
While music therapy did not significantly alter weaning duration (p = 0.97), it led to significant improvements in agitation and sedation levels on days 1 and 3 (p = 0.048, 0.005, and 0.024) and reduced tracheostomy rates (14%; p = 0.025). Patients receiving music therapy also required a lower percentage of propofol (p = 0.028) and showed a trend towards shorter total IMV and ICU stays, though these were not statistically significant.
Why It Matters
Music therapy may serve as a nonpharmacological adjunct in the ICU by reducing agitation, sedation, and the need for tracheostomy during mechanical ventilation weaning. Although not reaching statistical significance, the observed trends suggest potential benefits in decreasing perceived stress and overall ventilation and ICU durations. Further research is warranted to confirm these potential benefits.
Healthcare machine learning6 articles
Score 0.84 · ⚠ Check
Lei Chen et al. — npj Digital Medicine · October 21, 2026
Why it’s here: This article presents a Generative AI-powered navigation system for lung biopsies, validated in a randomized controlled trial, demonstrating real-world deployment and improved clinical outcomes.
⚠ a section is not grounded in the abstract (The 'Why It Matters' section contains unsupported sentences. While the abstract mentions the system facilitates a more standardized and efficient operational process and demonstrates promising clinical and translational value, it does not explicitly state that 'These results indicate the potential for AI-powered navigation to improve lung biopsy procedures, as evidenced by the observed reductions in key procedural metrics and complications.' This is an interpretation and extrapolation beyond the direct statements in the abstract.)
Study Design
This multicenter randomized controlled trial developed and validated a Generative AI-Powered Puncture Surgery Navigation System for lung punctures, termed GPS-Lung. The study evaluated its clinical applicability in guiding lung puncture biopsies, particularly for small and complex lesions.
Key Results
The GPS-Lung system demonstrated significant improvements in surgeon performance, reducing the number of punctures (2.0 ± 1.2 vs. 2.5 ± 1.7, P = 0.005) and operative time (20.4 ± 5.6 min vs. 23.2 ± 6.3 min, P = 0.001). It also decreased radiation exposure (4357.4 ± 3427.3 μGy·m 2 vs. 5570.9 ± 4846.7 μGy·m 2 , P = 0.033) and pneumothorax complications (12.7% vs. 23.9%, P = 0.033).
Why It Matters
The GPS-Lung system shows promising clinical and translational value in lung nodule puncture intervention by facilitating a more standardized and efficient operational process. These results indicate the potential for AI-powered navigation to improve lung biopsy procedures, as evidenced by the observed reductions in key procedural metrics and complications.
Score 0.82 · ✓ Verified
GUARD-LGG: a glioma upfront AI-driven radiotherapy response decipher for lower-grade glioma
Zihan Wang et al. — npj Digital Medicine · October 20, 2026
Why it’s here: This article describes a machine learning model for predicting radiotherapy response in glioma, including prospective validation and real-world deployment considerations.
Study Design
This study presents GUARD-LGG, a machine learning model developed to predict early radiotherapy (RT) response in lower-grade glioma (LGG) before treatment initiation. The model was trained on 963 patients from a major center and validated on independent external and prospective clinical trial datasets comprising 187 and 328 patients, respectively.
Key Results
GUARD-LGG demonstrated strong predictive performance across all datasets, achieving area under the curve (AUC) values of 0.946 in the training set, 0.929 in internal validation, 0.882 in the external test set, and 0.913 in the prospective test set. Key predictors identified included residual tumor, age, T2-weighted texture roughness, and T1-weighted contrast intensity changes.
Why It Matters
GUARD-LGG offers a tool for individualized prediction of early RT response in LGG prior to therapy, enabling pre-treatment response stratification. Radiogenomic analyses suggest potential links between predicted unfavorable response and RT resistance pathways, with patients predicted to have an unfavorable response exhibiting shorter overall survival.
Score 0.82 · ✓ Verified
Large language models for multimodal clinical predictive modeling: a systematic review
Levi Kaster et al. — npj Digital Medicine · October 22, 2026
Why it’s here: This systematic review specifically covers LLMs for multimodal clinical predictive modeling using EHR data, directly aligning with the researcher's interests.
Study Design
This systematic review characterized studies utilizing large language models (LLMs) for multimodal predictive modeling with electronic health record (EHR) data. Following a screening of 641 studies, 57 met the inclusion criteria for analysis.
Key Results
Three strategies for LLM-based multimodal integration were identified: embedding, extraction, and text fusion, all of which yielded performance gains averaging a 6.4% increase over single modality models. LLMs facilitated modeling by generating note-embeddings, extracting clinically informative variables, and summarizing long text documents.
Why It Matters
This study supports the potential value of LLMs for EHR-based predictive modeling and provides a framework for future multimodal modeling tools. Key considerations for LLM-enhanced predictive modeling include sample size, interpretability, and computational costs.
Score 0.78 · ✓ Verified
Chun-Nan Hsu et al. — npj Digital Medicine · October 19, 2026
Why it’s here: Develops and validates a deep learning model using EHR data and language modeling to predict opioid use disorder, demonstrating predictive modeling and external validation.
Study Design
This study developed OUD-REY, a deep learning model utilizing longitudinal electronic health record (EHR) data to predict opioid use disorder (OUD) risk at one year for patients with an opioid prescription. The model leveraged a pretrained language model to handle thousands of unseen clinical features. It was validated internally on the All of Us research program data and externally on a temporally separated All of Us dataset and a large Veterans Affairs (VA) dataset.
Key Results
On internal validation, OUD-REY achieved an AUROC of 91.2% and AUPRC of 77.0%. External validation on a temporally separated All of Us set yielded an AUROC of 86.4% and AUPRC of 23.1% (2.9% OUD prevalence), while the VA dataset showed an AUROC of 85.5% and AUPRC of 19.5% (3.5% OUD prevalence). Fairness auditing indicated the model was generally fair across different genders, race, and ethnic backgrounds.
Why It Matters
The OUD-REY model demonstrates strong predictive performance on both internal and external EHR datasets, including a large VA dataset, suggesting potential for broad application. Its ability to incorporate unseen clinical features and its general fairness across demographic groups may aid in improving OUD prevention and care. The model's performance on the VA dataset, containing ~6 million patients over 20 years, highlights its potential for real-world application.
Score 0.72 · ✓ Verified
Jiaqi Qiang et al. — npj Digital Medicine · October 22, 2026
Why it’s here: This article proposes a hybrid AI framework for facial detection of Turner Syndrome, validated prospectively, demonstrating AI for screening and clinical decision support.
Study Design
This study proposes a hybrid AI framework for Turner Syndrome (TS) detection by integrating deep feature learning and geometric prior knowledge, specifically designed to address data scarcity. The model was retrospectively constructed and then validated in a prospective cohort, aiming to identify patients with characteristic facial features.
Key Results
The proposed framework achieved high performance metrics, including 95.41% accuracy, 96.55% sensitivity, and 95.24% specificity, significantly simplifying the diagnostic process. Notably, when training focused on a hard subset of data, the sensitivity reached 100.00%, indicating perfect identification of all patients in that group.
Why It Matters
This AI facial recognition method offers a low-cost and accessible approach for TS screening, potentially facilitating earlier detection and timely interventions, especially in resource-limited settings. The novel paradigm also offers insights for using facial recognition in diagnosing other diseases.
✦ Something Different · Score 0.66 · ✓ Verified
Junseong Lee et al. — npj Digital Medicine · October 20, 2026
✦ A change of pace: This article explores the human element of AI collaboration by examining how expertise influences automation bias and sentinel behavior, offering a crucial perspective beyond purely technical AI performance.
Study Design
This multi-reader crossover study analyzed 1750 interpretation events of neonatal pneumoperitoneum radiographs. Clinicians reviewed images aided by either a high-reliability AI model or a model with injected errors.
Key Results
High AI reliability paradoxically induced automation bias in trainees, who accepted 52.0% of incorrect suggestions, while experts showed limited gains. Conversely, neonatologists exhibited a "sentinel behavior" phenotype, correctly overriding 91.7% of flawed AI errors.
Why It Matters
Clinical validity depends on the human-AI team's adversarial resilience, not standalone accuracy, suggesting a need to maintain diagnostic integrity under algorithmic failure. The findings are hypothesis-generating due to the small specialist cohort and warrant prospective replication, with an open-source educational sandbox released to mitigate deskilling risks.
Hospital logistics and operations6 articles
Score 0.72 · ✓ Verified
Fanwen Meng et al. — BMC Health Services Research · October 20, 2026
Why it’s here: Directly applies patient flow data and a graph-theoretic framework to design outpatient clinic layouts for operational efficiency.
Study Design
This study developed a graph-theoretic framework for facility layout planning in an eye specialist outpatient clinic. Patient visit data were analyzed to quantify movement patterns and interaction frequencies, represented as a weighted adjacency matrix. A maximal planar graph was then constructed using a modified greedy heuristic algorithm, incorporating clinical and operational input.
Key Results
Analysis of patient flows revealed strong connectivity among key service stations for visual assessment, diagnostic testing, consultation, and discharge. Two alternative block layouts were developed: a linear, sequential configuration for existing structures and a centralized consultation-core design for new facilities. Both designs emphasized connectivity, flexibility, and efficient patient circulation based on real patient journeys.
Why It Matters
This study demonstrates a practical, data-driven approach to outpatient clinic layout planning by integrating patient flow data with graph-theoretic modeling and clinical input. The framework identifies high-priority adjacency relationships and translates them into candidate block-layout configurations. While applied to an eye clinic, the framework is adaptable to other outpatient settings with sequential investigations, though the resulting layouts are planning concepts, not validated floor plans.
Score 0.72 · ✓ Verified
EXPRESS: What Drives Physician Efficiency? Evidence from Emergency Department Operations
Raha Imanirad et al. — Production and Operations Management · October 23, 2026
Why it’s here: Directly investigates physician efficiency in emergency departments using operations research methods like DEA.
Study Design
This study investigates the determinants of emergency department (ED) physician efficiency using a multi-stage framework. Data from 110,325 patient visits at a leading U.S. hospital were analyzed using Data Envelopment Analysis (DEA) to measure individual physician efficiency. A Tobit regression model was then employed to examine the influence of physician experience, shift familiarity, and patient severity on this efficiency.
Key Results
Physician efficiency was found to increase with experience at a diminishing rate and vary with shift familiarity, though this association weakened with greater experience. Higher patient severity was associated with lower physician efficiency, but this negative association was attenuated for more experienced physicians. Specifically, experienced physicians were better able to maintain efficiency when managing more demanding clinical conditions.
Why It Matters
These findings offer actionable recommendations for improving ED operations, suggesting staffing strategies that assign more experienced physicians to higher-severity patients and scheduling strategies that emphasize consistency for less experienced physicians. Simulation results illustrate potential reductions in physician resource use from these evidence-based staffing and scheduling approaches. The study highlights the value of optimizing physician staffing and scheduling to enhance ED efficiency.
● Preprint · Score 0.72 · ✓ Verified
de Araujo, F. A. S. et al. — medRxiv Preprint · health economics · October 21, 2026
Why it’s here: Evaluates the impact of Lean Healthcare on Emergency Departments, directly addressing process efficiency, workflows, hospitalizations, and length of stay.
Study Design
This study employed a staggered difference-in-differences approach to evaluate the impact of Lean Healthcare in Emergency Departments (EDs) across 309 Brazilian hospitals. The intervention was implemented between January 2017 and December 2025, analyzing hospital-level indicators for hospitalizations and average length of stay for emergency admissions.
Key Results
The intervention was associated with an average increase of 38.6 admissions per hospital per month (a 5.9% increase) and a reduction of 0.184 days in the average length of stay (a 3.0% decrease). The effect on admissions emerged gradually with intervention duration, while the reduction in length of stay was evident earlier.
Why It Matters
These findings provide evidence of a causal effect of Lean Healthcare on operational performance, suggesting improved bed productivity and effective capacity without physical infrastructure expansion. The results support flow-management methodologies as a strategy to mitigate ED overcrowding and increase service capacity, with implications for hospital capacity planning.
Score 0.70 · ⓘ Reference
Won-Chol Kim et al. — Health Care Management Science · October 26, 2026
Why it’s here: Directly addresses integrated scheduling of surgeries using a stochastic optimization model, a core topic in hospital operations research.
Abstract unavailable — listed as a pointer; not summarized.
★ Flagship · Score 0.67 · ✓ Verified
Cross-Cluster Referrals and Subsidy Schemes in Multi-Cluster Healthcare Systems
Liming Liu et al. — Manufacturing & Service Operations Management · October 21, 2026
Why it’s here: This article directly addresses healthcare system performance, waiting times, referrals, and resource allocation, which are core to hospital operations and logistics.
Study Design
This study constructs a multi-cluster service network model to analyze optimal routing policies for public healthcare systems facing excessive waiting times. The research examines both individual and system optimization perspectives, defining and analyzing externalities within this network setting. Key methods involve identifying conditions for effective cross-cluster referrals and evaluating subsidy schemes.
Key Results
The study finds that cross-cluster referrals can be effective in reducing system costs when workload imbalance is substantial but system congestion is not excessive, and lowering switching costs can paradoxically degrade performance. While fixed subsidies generally cannot fully coordinate a multi-cluster system due to cross-cluster externalities, they can effectively reduce system costs across various parameter values. Dynamic, state-dependent subsidies are shown to achieve perfect alignment between individual and system optimization.
Why It Matters
These findings suggest that cross-cluster referrals and opt-out/switching subsidies should be implemented with careful operational control, promoting referrals during high workload imbalance but discouraging them under very high overall system workload. Fixed subsidies can improve performance under different demand conditions, with a strategic focus on switching subsidies for moderate workload and substantial imbalance, and opt-out subsidies for high overall workload.
★ Flagship · ✦ Something Different · Score 0.67 · ✓ Verified
Xianjia Gao, Xin Chen — Manufacturing & Service Operations Management · October 21, 2026
✦ A change of pace: This article explores food bank logistics, offering a novel perspective on supply chain management and waste reduction that is distinct from typical hospital-focused operational studies.
Study Design
This research addresses the operational challenges of food banks in allocating uncertain and perishable donations to maximize community benefit. A comprehensive dynamic programming framework was developed to model this complex allocation problem, leading to two practical policies: a static Scaled Fluid (SF) policy and an adaptive Re-Optimized (ReOPT) policy.
Key Results
Both the SF and ReOPT policies are proven to be asymptotically optimal through rigorous theoretical proofs. Extensive simulations using both synthetic and real data demonstrate that these proposed strategies yield significant utility gains.
Why It Matters
This research offers food bank managers theoretically sound and empirically validated tools to enhance operational efficiency, effectiveness, and equity in food distribution. The framework provides a clear path for organizations to better serve their communities by improving decision-making with available resources.
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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