Introduction: Gastrointestinal (GI) cancers are associated with high morbidity and mortality. Surgical resection, the primary treatment, often induces immunosuppression and increases the risk of postoperative complications. Perioperative immunonutrition (IMN), comprising formulations enriched with omega-3 fatty acids, arginine, nucleotides, and antioxidants, has emerged as a potential strategy to improve surgical outcomes by reducing complications, enhancing immune function, and promoting recovery. Methods: A systematic search of PubMed, Scopus, and the Cochrane Library was conducted on 28 October 2024 in accordance with PRISMA guidelines. Systematic reviews and meta-analyses evaluating perioperative IMN versus standard care in adult patients undergoing GI cancer surgery were included in the search. The outcomes assessed included infectious and non-infectious complications, wound healing, hospital stay, and nutritional status. The study quality was evaluated using AMSTAR 2, and the meta-analysis was conducted using random-effects models to calculate the pooled effect sizes (risk ratios [RRs], odds ratios [ORs], mean differences [MDs]) with 95% confidence intervals (CIs). Results: Sixteen systematic reviews and meta-analyses, including a total of 41,072 patients, were included. IMN significantly reduced infectious complications (RR: 0.62, 95% CI: 0.55–0.70; I² = 63.0%), including urinary tract infections (RR: 0.74, 95% CI: 0.61–0.89; I² = 0.0%) and wound infections (OR: 0.64, 95% CI: 0.55–0.73; I² = 34.4%). Anastomotic leak rates were notably lower (RR: 0.68, 95% CI: 0.62–0.75; I² = 8.2%). While no significant reduction in pneumonia risk was observed, non-infectious complications decreased significantly (RR: 0.83, 95% CI: 0.75–0.92; I² = 30.6%). IMN also reduced the length of hospital stay by an average of 1.92 days (MD: −1.92, 95% CI: −2.36 to −1.48; I² = 73.5%). Conclusions: IMN provides significant benefits in GI cancer surgery, reducing complications and improving recovery. However, variability in protocols and populations highlight the need for standardization and further high-quality trials to optimize its application and to validate its efficacy in enhancing surgical care.

Introduction Neural anastomoses between the median nerve (MN) and ulnar nerve (UN), including the Berrettini anastomosis (BA), influence the sensory innervation of the hand. Although typically asymptomatic, the BA's anatomical variability can have clinical implications, particularly during surgical procedures. This study aimed to investigate the prevalence, anatomical variations, and probable location of the BA in cadaveric hands. Methods A total of 104 fresh Caucasian cadaveric hands (52 donors: 26 males, 26 females) were dissected at the Tilman J. Fertitta Family College of Medicine and Baylor College of Medicine, Houston, TX. The dissections focused on the BA's origin, insertion, and branching patterns. Measurements were taken using vertical and horizontal reference points to map its "probable zone." Data were collected by multiple researchers to ensure accuracy, and discrepancies were resolved by averaging measurements. Prevalence and variations were categorized into five distinct types, with detailed illustrations provided. Results The BA was identified in 80% (83/104) of hands. Type I, with a single connection from the UN to the MN, was the most common (38%). Other patterns included bifurcating (type II), dual origin (type III), reverse (type IV), and complex fibrous networks (type V). The average BA length was 22.03 ± 7.68 mm, with its "probable zone" spanning 26.11-44.21% of hand width and 36.63-50.21% of hand length. Conclusion The BA is a common neural connection with significant anatomical variability. Identifying its "probable zone" provides valuable guidance for surgical procedures, minimizing the risk of sensory deficits in the hand. Further studies are recommended to expand upon these findings.

Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk–benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal. Results: Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting. Conclusions: AI demonstrates early promise in augmenting risk–benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes.

Introduction: Clinical practice guidelines (CPGs) rely on moderate- and high-quality randomized controlled trials (RCTs) and make binary recommendations (effective or not effective). Health technology assessment (HTA) reports include non-randomized studies of interventions (NRSIs). In addition to assessing effectiveness, HTA reports assess harms and safety, differential effectiveness, and cost-effectiveness. The Washington State Health Care Authority (HCA) uses the Health Technology Clinical Committee (HTCC) to decide which technologies will be covered and under which clinical conditions. Methods: Ninety-two HTA reports and the HTCC coverage and conditions decisions from the Washington State HCA were reviewed from inception through January 31, 2025. Thirty-five HTA reports related to musculoskeletal technologies were reviewed and data abstracted including report date, topic, subtopic, comparisons, number of RCTs for each decision/comparison, number of NRSIs for each decision/comparison, highest effectiveness quality of evidence (QoE) for each decision/comparison, and coverage decision. HTCC coverage decisions were compared to American Academy of Orthopaedic Surgeons (AAOS) CPG recommendations to assess the congruence between HTCC coverage decisions and AAOS CPG recommendations. Results: Thirty-eight percent (35/92) of the HTA evidence reports addressed musculoskeletal technologies. The 35 HTA reports resulted in 61 coverage decisions. Thirty-six of the 61 (59%) reviewed technologies were not covered, 25 (41%) were covered with conditions, and no technology was covered unconditionally. If a technology was likely to be covered unconditionally, there would be no need for review. Of the technologies covered with conditions, the highest quality of evidence supporting the coverage decision was high-quality evidence – 8% (2/25), moderate-quality evidence – 32% (8/25), low-quality evidence – 40% (10/25), and very low-quality evidence – 12% (3/25). The most common subspecialties or joints that were included in the coverage decisions were spine (31%), knee (20%), and hip (11%). Eleven technologies had applicable AAOS CPG recommendations. Six of the AAOS CPG recommendations were congruent with HTCC coverage decisions. Of the five non-congruent decisions, four had limited evidence to support the recommendation and one recommendation was inconclusive. Conclusions: Clinical practice guidelines developed with GRADE methodology provide binary recommendations: effective or not effective. GRADE methodology automatically downgrades observational studies to low-quality evidence. Observational studies (prognostic evidence) are needed to determine coverage conditions and for shared decision-making. The process used by the Washington State HCA HTCC could complement CPGs by providing more nuanced, condition-specific guidance. The inclusion of safety and harms, differential effectiveness, and cost-effectiveness provides a robust assessment of technologies. Standardized coverage conditions would simplify pre-authorization.