Evaluating Intraoperative Laparoscopic Skill: Direct Observation Versus Blinded Videotaped Performances
Bibliographic record
Abstract
The Global Operative Assessment of Laparoscopic Skill (GOALS) has been shown to meet high standards for direct observation. The purpose of this study was to investigate the reliability and validity of GOALS when applied to blinded, videotaped performances. Five novice surgeons and 5 experienced surgeons were each evaluated by 2 observers during a laparoscopic cholecystectomy. Subsequently, 4 laparoscopists (V1 to V4) evaluated the videotaped procedures using GOALS. Two of the raters (V1 and V3) had prior experience using GOALS. The interrater reliabilities between video raters (VRs) and between VRs and direct raters (DRs) were calculated using the intraclass correlation coefficient. Construct validity was assessed using 2-way analysis of variance. Interrater reliability between the 4 VRs and the 2 DRs was 0.72. The intraclass correlation coefficient for the 4 VRs was 0.68 and for each VR compared with the mean DR was 0.86, 0.39, 0.94, and 0.76, respectively. All raters, except V2, differentiated between novice and experienced groups (P values ranged from .01 to .05). These data suggest that GOALS can be used to assess laparoscopic skill based on videotaped performances but that rater training may play an important role in ensuring the reliability and validity of the instrument. Experience with the tool in the operating room may improve the reliability of video rating and could be of value in training evaluators.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".