Value of an objective assessment tool in the operating room
Bibliographic record
Abstract
BACKGROUND: Concerns about the achievement of surgical proficiency during residency are increasing. To objectify surgical skills, the Objective Structured Assessment of Technical Skills (OSATS) was developed and proven valid, feasible and reliable for use in laboratory settings. This study aimed to evaluate the value of this tool for intraoperative use. METHODS: Residents were assessed with an OSATS after every procedure they performed as the primary surgeon during a 3-month clinical rotation in gynecological surgery. We mapped individual learning curves (OSATS scores plotted against experience) and established the average procedure-specific learning curve. We used linear mixed models to assess the relation between performance and experience. RESULTS: Nine residents were recruited and 319 OSATS analyzed. Individual learning curves revealed progression beyond 24 of 30 OSATS points for 7 residents. Performance on the average procedure improved with experience, and the OSATS score increased by an average of 1.10 points per assessed procedure (p=0.008, 95% confidence interval 0.44-1.77). Median OSATS scores ranged from 18 to 30 among the 21 assessors. CONCLUSION: Intraoperative implementation of OSATS seems to offer important advantages: structured feedback is facilitated, and learning curves enable insight into individual progression. However, doubts have been raised about the objectivity of the tool. Therefore, caution is warranted in using it for graduation and certification.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".