Exploring a novel approach to assessing surgical team collaboration: Evidence of brain activity synchronization
Bibliographic record
Abstract
Interpersonal brain synchronization (IBS) has emerged as a significant concept in understanding collaborative team dynamics, with functional near-infrared spectroscopy (fNIRS) proving to be a vital tool in its assessment. This review aims to collate and analyze the literature on the application of fNIRS in various team settings, emphasizing its potential utility in surgical environments. A thorough search and screening process across multiple databases resulted in 17 studies being reviewed, with a focus on the utilization of fNIRS to measure IBS in different collaborative tasks. This review examined the tasks employed, participant demographics, organizational structures of teams, methodologies for IBS measurement, and correlations between brain synchronization and behavioral measurements. FNIRS emerged as a non-invasive, cost-effective, and portable tool, predominantly used to assess IBS in pair-based tasks with a variety of participant demographics. Wavelet transform coherence was the primary method used for measuring synchronization, particularly in the prefrontal brain region. A consistent correlation was found between increased brain synchronization and enhanced team performance, underscoring the potential of fNIRS in understanding and optimizing team dynamics. This review establishes fNIRS as a promising tool for investigating the neural mechanisms underlying team cooperation, providing invaluable insights for potential applications in surgical settings. While acknowledging the limitations in the current literature, the review highlights the need for further research with larger sample sizes and varied task complexities to solidify the understanding of IBS and its impact on team performance. The ultimate goal is to leverage fNIRS in assessing and improving surgical team dynamics, contributing to improved patient outcomes and safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".