Using a Comprehensive Examination to Assess Multiple Competencies in Surgical Residents: Does the Oral Examination Still Have a Role?
Bibliographic record
Abstract
BACKGROUND: While specialty-level evaluations evolve from traditional examinations to objective structured clinical examination-like assessments, a broader range of competencies are tested; consequently, examiners are forced to integrate results when making a determination of competency. The aim of this study was to describe how experts weigh relative performances on specific components of a comprehensive examination to make decisions of overall competency. STUDY DESIGN: The Patient Assessment and Management Examination is a standardized patient-based assessment of general surgery in which each 25-minute station encompasses four components: history and physical examination, investigation interpretation, diagnosis and treatment discussion with the patient, and a structured oral examination (SOE). A six-station Patient Assessment and Management Examination was administered to 21 senior surgery residents. Surgeons marked each station with global rating scales and, in addition, provided an end-of-station overall global assessment of performance. A "gold-standard" examination pass-or-fail decision was determined through videotape review of each candidate's performance across six stations by two blinded surgeons. Multiple linear regression analysis was used to determine which components were associated with the end-of-station overall global assessments. Multivariable logistic regression was used to determine which components were associated with the final "gold-standard" pass-or-fail assessment. RESULTS: The only component notably (p < 0.005) associated with end-of-station global assessment for all six stations was the SOE. Mean SOE score was the only notable independent variable associated with the gold-standard pass-or-fail decision (R(2) = 0.63, p < 0.001). CONCLUSIONS: Performance on the SOE section of a multicompetency examination is markedly associated with the final determination of competency. These results have implications for the design and implementation of comprehensive specialty-level assessments.
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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 | 0.006 | 0.033 |
| 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".