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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".