Relationship between objective assessment of technical skills and subjective in-training evaluations in surgical residents
Bibliographic record
Abstract
BACKGROUND: Technical skills of residents have traditionally been evaluated using subjective In-Training Evaluation Reports (ITERs). We have developed the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS), an objective measure of laparoscopic technical ability. The purpose of the study was to assess the concurrent validity of the MISTELS by exploring the relationship between MISTELS score and ITER assessment. STUDY DESIGN: Fifty surgery residents were assessed on the MISTELS system. Concurrent ITER assessments of technical skill were collected, and the proportion of superior ratings for the year was calculated. Statistical comparisons were performed by ANOVA and chi-square analysis. The Pearson correlation coefficient was used to compare the scores in the MISTELS with the ITER ratings. RESULTS: The 50 residents received 277 ITERs for the year, of which 103 (37%) were "superior," 170 (61%) "satisfactory," 4 (1%) "borderline," and 0 "unsatisfactory." The MISTELS score correlated moderately well with the proportion of superior ITER scores (r = 0.51, p < 0.01). Residents who passed the MISTELS had a higher proportion of superior ITER assessments than those who failed the MISTELS (p = 0.02), but residents who performed below their expected level on the MISTELS still received mainly satisfactory ITERs (82 +/- 18%). CONCLUSIONS: The ITER assessment is poor at identifying residents with below-average technical skills. Residents who perform well in the MISTELS laparoscopic simulator also have better ITER evaluations, providing evidence for the concurrent validity of the MISTELS. Multiple assessment instruments are recommended for assessment of technical competency.
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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 | 0.005 | 0.043 |
| 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".