Conectar
by Chanseo Lee, Jaihyoung Lee, Kimon-Aristotelis Vogt, Muhammad Munshi
BackgroundAccurate intraoperative detection of nociceptive events is essential for optimizing analgesic administration and improving postoperative outcomes. Although deep learning approaches promise improved modeling of complex physiologic dynamics, their added computational and operational complexity may not translate into clinically meaningful benefit, particularly in small, high-resolution perioperative datasets.
MethodsWe performed a head-to-head evaluation of classical supervised models (L1-regularized logistic regression and 50-, 200-tree Random Forests, with and without drug dosing features) against a Temporal Convolutional Network (TCN) transfer-learning framework for intraoperative nociception detection. Using 101 adult surgical cases with 30 physiologic and 18 drug dosing features sampled in 5-second windows, models were assessed under leave-one-surgery-out cross-validation using AUROC and AUPRC. We further examined probability calibration, multiple ensemble strategies, permutation importance features, and computational cost in terms of inference operations and memory footprint.
ResultsDrug-aware Random Forests of various trees (50 trees vs. 200 trees) achieved the highest discrimination (AUROC 0.716; AUPRC 0.399), outperforming the TCN transfer-learning model (AUROC 0.649; AUPRC 0.311). However, increasing personalization windows in the TCN yielded inconsistent and modest gains (p > 0.05). Isotonic calibration substantially improved probability calibration but did not affect discrimination. No ensemble method surpassed the standalone Random Forest; the gated network consistently assigned >84% weight to the classical model. Computational analysis revealed that while the TCN was more compact in total memory footprint, the smaller, 50-tree Random Forest inference required two orders of magnitude fewer operations, with faster training and lower operational complexity.
ConclusionsIn this clinically realistic benchmark, interpretable classical models operating on well-engineered features without personalization matched or exceeded the performance of a personalized deep learning approach while remaining computationally cheaper and simpler to deploy. These findings underscore the importance of rigorously justifying model complexity in perioperative machine learning and suggest that, for intraoperative nociception monitoring, classical approaches may offer a more favorable balance of accuracy, interpretability, and operational efficiency.
This study evaluates how participants experienced and assessed a three-round Delphi study on the terminology of developmental language disorders in childhood. It compares participants who completed all rounds (completers) with those who withdrew early (dropouts) and aims to derive methodological quality criteria for future Delphi studies.
The evaluation is based on a Delphi study conducted in 2021/2022 across five German-speaking countries. After the final round, n=179 experts (40% response rate) completed a standardised survey assessing their expertise, motivation, reasons for discontinuation, time commitment and perceptions of questionnaire and feedback design. Responses from completers (n=156) and dropouts (n=23) were analysed descriptively.
Most participants had no prior experience with Delphi methods but rated the study positively and considered the topic highly relevant. Completers reported their subjective time commitment to be lower and rated the handling of the questionnaire more positively than dropouts. Feedback was used by nearly half of all experts and was more actively considered by completers. Lack of time was the most common reason for discontinuation.
The findings confirm the feasibility and acceptance of the Delphi method in interdisciplinary health research. In addition to established methodological principles, topic relevance, clear communication and time commitment emerged as key areas for expert motivation and engagement.
Blue light (peak wavelength 442 nm) has been shown to modulate the immune response in preclinical models of intra-abdominal sepsis and pneumonia. In vivo pathways involve optic nerve stimulation with transmission to the central nervous system, activation of parasympathetic pathways terminating at the spleen, and downstream immune effects including decreased inflammatory tissue damage and improved pathogen clearance. Related effects on pain mediators including proinflammatory cytokines (interleukin 6, TNF- α) and autonomic tone (increased parasympathetic outflow) suggest possible analgesic properties that would be highly relevant to a trauma population.
This is a randomised controlled trial in which adult trauma inpatients (
Full ethical approval for this trial has been granted by the University of Pittsburgh Institutional Review Board. On study completion, results will be published in the peer-reviewed literature and at ClinicalTrials.gov.
In moderate to high-risk surgical procedures, 15–25% of patients develop a postoperative surgical site infection. Intraoperative incisional wound irrigation has the potential to reduce surgical site infections, and additional randomised controlled trials are required to provide evidence of effectiveness.
This protocol describes a pragmatic, adaptive, participant and adjudicator-blinded trial at 13 sites in Canada in up to 2500 participants. Participants planned for surgery with an abdominal or groin incision, who are eligible and provide verbal consent through an integrated consent model, are randomised to receive intraoperative incisional wound irrigation with povidone-iodine, saline or no irrigation. The primary outcome is surgical site infection within 30 days postoperatively. Secondary outcomes include quality of life measured 30 days postoperatively and morbidity, mortality and healthcare utilisation within 90 days postoperatively.
This trial has been approved by the research ethics board at the participating centres and stopped enrolling participants on May 23, 2025. All participants will provide verbal consent. Results will be disseminated via presentation at conferences, publication and posted on clinicaltrials.gov.
The study is registered with http://clinicaltrial.gov (
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