Citations

Abstract Hypoxia constrains cerebral oxygen availability and challenges brain function and stability. Although hypoxia-responsive functional connectivity (HR-FC) reorganizes rapidly with declining arterial oxygen partial pressure, its relationship to local neurovascular activity remains unclear. We examined time-resolved amplitude of low-frequency fluctuations (ALFF) in blood-oxygenation-level dependent (BOLD) fMRI during graded acute hypoxia in healthy adults, performing a continuous cognitive test (Go/No-go task) with concurrent physiological monitoring. Dynamic ALFF and functional connectivity were estimated using a sliding-window approach and analyzed across large-scale brain networks defined by Schaefer’s 17-network parcellation. Severe hypoxia elicited temporally dissociated responses across modalities. Functional connectivity increased monotonically, whereas ALFF exhibited pronounced nonlinear modulation, including phase-dependent divergence across networks. During hypoxic decompensation, the default mode network (DefaultA) showed marked ALFF suppression, whereas a ventral secondary somatosensory-dominant network (SomMotB) exhibited preferential preservation despite similar engagement in HR-FC. Together, these findings indicate that network-level ALFF captures a distinct yet complementary layer of functional dynamics, with a temporal profile distinct from functional connectivity. Spontaneous BOLD dynamics during acute hypoxia reflect structured network-level modulation rather than a uniform suppression attributable solely to reduced oxygen availability. These findings support a conceptual framework of cerebral oxygen budgeting , in which metabolic constraints reshape functional dynamics across brain networks.

Background: Large language models (LLMs) are being used by surgeons for education and reference yet concerns about hallucinations and reliability limit safe adoption. Retrieval-augmented generation (RAG) can offer a potential solution by grounding responses in a high-quality external database (e.g., medical textbooks) to enhance accuracy. However, performance tradeoffs across different RAG configurations—many of which exponentially increase computational cost—remain poorly characterized. Methods: In total, 120 lightweight, open-source RAG configurations were evaluated across 40 plastic surgery-focused question-answering tasks (20 single-hop, 20 multi-hop), spanning multiple subspecialties (4800 total evaluations). Configurations varied by base LLM (Phi-3-mini-128k-instruct vs. BioMistral-7B), embedding model, database size, chunk size, and query hop type. Performance was assessed using semantic similarity (Ragas) to physician-validated reference answers. Performance was analyzed using linear mixed-effects regression with query as a random effect and fixed and interaction effects selected via likelihood testing and AIC. Results: High performance was achievable using lightweight, open-source models. While BioMistral-7B had high mean sematic similarity under specific configurations (mean semantic similarity up to 0.786), Phi-3-mini-128k-instruct demonstrated more consistent performance across query complexity. Larger database sizes significantly improved semantic similarity, with the largest gain at intermediate sizes (e.g., size 5: +0.043, p = 0.001). Embedding choice had a strong effect, with bge-large-en-v1.5 improving performance (p = 0.0016) and Bio_ClinicalBERT markedly reducing it (p < 0.001). Multi-hop queries substantially reduced performance (p < 0.001), though this effect was attenuated for Phi-3-mini-128k-instruct via a strong model × hop-type interaction (p < 0.001). Conclusions: RAG systems for plastic surgery do not require large proprietary models, as performance depends on configuration choices and interaction effects rather than isolated components. With advancements, predictive modeling may enable resource-efficient, safe deployment of clinical RAG systems.

Background: Automated discharge instructions are increasingly used to support post-discharge communication, patient education, and nursing follow-up, yet the current state remains unidentified. This systematic review explores the types of automated discharge instructions used and their effectiveness in enhancing patient engagement and reducing readmission, emergency department visits and reoperation rates. Methods: A systematic search was conducted on 15 April 2025, using Embase, PubMed, Scopus, Web of Science, and CINAHL, following PRISMA guidelines. Inclusion criteria required peer-reviewed original research evaluating the utilization of automated patient discharge instructions following hospital admission or surgical stay. Exclusion criteria included correspondence, reviews, educational materials, not peer-reviewed, retracted reports, not retrievable, and no English translation. Risk of bias was assessed independently using NIH, JBI, ROB-2, and ROBINS-I tools. Two investigators independently conducted the screening, extraction, and synthesis of results using Endnote and Microsoft Excel. Results: Of the 1252 records identified, 13 studies were selected for analysis. There was a total of 34,386 patients across a diverse range of healthcare settings and clinical contexts. The average sample size per study was approximately 4912, with study samples ranging from 16 to 13,188 patients. The modalities of discharge instructions included automated phone calls (23.1%) and/or text messages (53.8%), as well as printed out auto-generated summaries (15.4%). Patient engagement was generally high, with automated phone calls showing the most consistent interaction, with completion rates ranging from 44% to 56%, often prompting clinical follow-up. SMS tools demonstrated strong scalability and response rates up to 87%. Two studies reported on hospital readmission outcomes and only a single study reported on emergency department revisit rates, while none assessed reoperation outcomes. Among those reporting readmission, automated phone calls and SMS were associated with lower or proxy-reduced readmission rates. Included studies had low to moderate levels of bias. Conclusions: While evidence on clinical outcomes such as readmissions, emergency department revisits, and reoperations remains limited and inconclusive, automated discharge tools—particularly phone calls and SMS—consistently demonstrated high patient engagement. Automated discharge tools show promise for supporting transitional care, discharge education, and post-discharge monitoring, highlighting the future role of automated tools in nursing workflows to support follow-up, escalation, and continuity of care.

aims: We assessed the diagnostic accuracy of the SVEAT score in predicting 30-day major adverse cardiac events (MACE) in comparison with the well-established HEART and TIMI scores. background: Acute chest pain (ACP) is a common symptom in emergency departments. Various risk stratification tools have been developed to assess patients presenting with ACP. The Simple Vascular Event Assessment Tool (SVEAT) is the most recent scoring system introduced, based on five variables: Symptoms of chest pain, history of Vascular disease, ECG, Age, and Troponin-I. objective: We assessed the diagnostic accuracy of the SVEAT score in predicting 30-day major adverse cardiac events (MACE) in comparison with the well-established HEART and TIMI scores. method: Following PRISMA guidelines, a systematic literature search was conducted across five databases until June 22, 2024. Four studies comprising 1,520 patients were included in this meta-analysis. The predictive accuracy of the SVEAT, HEART, and TIMI scores for 30-day MACE was assessed by the area under the receiver operating characteristic curve (AUC). Heterogeneity was evaluated using the I² statistic, with values over 75% indicating significant heterogeneity. Indirect comparisons among the scores were performed using the Z-test. result: Pooled AUC for the SVEAT score was 0.92 (95% CI: 0.86-0.98), indicating superior predictive accuracy compared to the HEART (AUC: 0.86, 95% CI: 0.81-0.92) and TIMI (AUC: 0.82, 95% CI: 0.69-0.96) scores. However, significant heterogeneity (I² = 90.6%) was observed across studies, potentially due to variations in patient demographics, clinical settings, and outcome definitions. Despite the higher AUC, indirect Z-test comparisons revealed no statistically significant differences between the scoring systems. conclusion: SVEAT score demonstrates potential as an accurate tool for predicting 30-day MACE in patients with ACP. other: N/A

(1) Background: Large language models (LLMs) show promise in healthcare but are constrained by hallucinations, static knowledge, and limited domain specificity. Fine-tuning (FT) and retrieval-augmented generation (RAG) offer complementary solutions, with FT embedding domain reasoning and RAG enabling dynamic, up-to-date knowledge access. Hybrid FT + RAG frameworks have been proposed to improve factual accuracy and clinical reliability. This scoping review synthesizes current evidence on such hybrids in healthcare AI. (2) Methods: The search across PubMed, IEEE Xplore, Google Scholar, and Embase identified studies implementing explicit FT + RAG hybrids in healthcare or biomedical tasks. Eligible studies reported empirical evaluations of LLM performance or behavior. Data were extracted on base models, FT strategies, RAG architectures, applications, and performance outcomes. (3) Results: Seven studies met inclusion criteria. FT + RAG systems consistently outperformed FT-only or RAG-only approaches across QA, clinical summarization, report generation, and decision support tasks. Parameter-efficient FT methods (e.g., LoRA) were common, while RAG implementations varied (dense, hybrid, hierarchical, multimodal, federated). Reported benefits included improved accuracy, reduced hallucination, and greater clinician preference and feasibility in protected settings. (4) Conclusions: FT + RAG frameworks represent a promising direction for clinically grounded healthcare AI, combining domain-specific reasoning with transparent, up-to-date retrieval. Future work should prioritize standardized evaluation, workflow integration, and governance to enable safe deployment.

Background: A critical component of conducting systematic reviews or meta-analyses is assessing the methodological quality and bias of included studies. Several methodological quality assessment tools have been developed; however, these tools may not be relevant to observational sports medicine research, which carries numerous unique nuances and biases. Purpose: To develop the Sport Publication Observational Research Tool (SPORT), which evaluates and scores the methodological quality of observational sports medicine research. Study Design: Consensus statement. Methods: SPORT was developed through a modified Delphi approach involving members from the Herodicus Society and The FORUM. All active members were invited to participate in the process aimed at building consensus on SPORT content and scoring. After finalizing SPORT, a power analysis led to the independent selection of 55 observational clinical sports medicine studies, which were scored twice by 4 reviewers of varying training levels. Interrater and intrarater reliability for SPORT was assessed using intraclass correlation coefficients (ICCs). The distribution and percentiles for total SPORT score across the 55 studies were calculated. SPORT was also compared with the methodological index for non-randomized studies (MINORS), a commonly utilized quality assessment tool. Results: A total of 51 members participated and achieved 100%, 100%, 98.0%, and 98.0% completion rates for rounds 1 through 4, respectively. The final SPORT included 19 subscores related to methodological quality and bias and achieved 94% consensus approval. Mean SPORT completion time was 6 minutes and 19 seconds per study, which varied significantly by reviewer training level. The subscore "peer review" demonstrated unacceptable reliability and was removed. The remaining 18 subscores exhibited ICC ranges of 0.599 to 0.955 for interrater reliability and 0.530 to 0.936 for intrarater reliability. Total SPORT score demonstrated excellent agreement, for interrater (ICC, 0.967) and intrarater reliability (ICC, 0.966). Median SPORT score across the 55 studies was 20.0 and skewed toward lower scores. There was a moderate significant correlation between SPORT and MINORS ( r [53] = 0.575; P < .001). Conclusion: An objective tool to assess the methodologic quality of observational sports medicine research (SPORT) was successfully developed through a modified Delphi approach with numerous content experts in the field. This tool may be useful in assessing the methodological quality of primary observational sports medicine studies included in systematic reviews and meta-analyses.

Background: Large language models (LLMs) are increasingly integrated into clinical documentation, decision support, and patient-facing applications across healthcare, including plastic and reconstructive surgery. Yet, their evaluation remains bottlenecked by costly, time-consuming human review. This has given rise to LLM-as-a-judge, in which LLMs are used to evaluate the outputs of other AI systems. Methods: This review examines LLM-as-a-judge in healthcare with particular attention to judging architectures, validation strategies, and emerging applications. A narrative review of the literature was conducted, synthesizing LLM judge methodologies as well as judging paradigms, including those applied to clinical documentation, medical question-answering systems, and clinical conversation assessment. Results: Across tasks, LLM judges align most closely with clinicians on objective criteria (e.g., factuality, grammaticality, internal consistency), benefit from structured evaluation and chain-of-thought prompting, and can approach or exceed inter-clinician agreement, but remain limited for subjective or affective judgments and by dataset quality and task specificity. Conclusions: The literature indicates that LLM judges can enable efficient, standardized evaluation in controlled settings; however, their appropriate role remains supportive rather than substitutive, and their performance may not generalize to complex plastic surgery environments. Their safe use depends on rigorous human oversight and explicit governance structures.