Nontechnical Skill Assessment of the Collective Surgical Team Using the Non-Technical Skills for Surgeons (NOTSS) System
Bibliographic record
Abstract
OBJECTIVE: To validate the Non-Technical Skills for Surgeons (NOTSS) system for assessment of the collective surgical teams' nontechnical skills after observing recordings of actual OR environment. BACKGROUND: The NOTSS system is a widely accepted tool to measure nontechnical skills of individual surgeons, and has mostly been used in the simulated setting. Surgical procedures are rarely performed by a single surgeon, but by a surgical team of attending surgeons, surgical assistants, and surgical trainees. Therefore, assessment of nontechnical skills may benefit from holistic assessment of the collective surgical teams. METHODS: Five trained participants assessed surgical team and attending surgeon using the NOTSS system after watching ten 20-minute long videos obtained from live OR. A set of reference ratings was provided by a multidisciplinary expert committee. We performed analyses to assess system sensitivity; examine inter-rater reliability of ratings; investigate concurrent construct validity; and assess feasibility and acceptability of using the NOTSS system to measure surgical team performance. RESULTS: There was adequate system sensitivity when comparing participants' and reference ratings. Inter-rater reliability among the participants' ratings was good except for decision-making category. The level of inter-rater reliability was similar when rating teams and attending surgeons. There was strong positive correlation between teams' and attending surgeons' NOTSS ratings at category [Pearson coefficient 0.86, 95% confidence interval (CI) 0.82-0.89] and element levels (0.83, 95% CI 0.80-0.85), demonstrating evidence of concurrent construct validity. The participants felt that the use of NOTSS system to measure teams' nontechnical skills was acceptable and feasible to a fair extent. CONCLUSION: The NOTSS system, although developed for assessment of individual surgeons, is a useful tool for observing and rating surgical teams.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".