Structured Assessment of Minor Surgical Skills (SAMSS) for Family Medicine Residents
Bibliographic record
Abstract
PURPOSE: To develop a valid and reliable examination to assess the technical proficiency of family medicine residents' performance of minor surgical office procedures. METHOD: A multi-station OSCE-style examination using bench-model simulations of minor surgical procedures was developed. Participants were a randomly selected group of 33 family medicine residents (PGY-1 = 16, PGY-2 = 17) and 14 senior surgical residents who functioned as a validation group. Examiners were qualified surgeons and family physicians who used both checklists and global rating scales to score the participants' performances. RESULTS: When family medicine residents were evaluated by family physicians, interstation reliabilities were .29 for checklists and .42 for global ratings. When family medicine residents were evaluated by surgeons, the reliabilities were .53 for checklists and .75 for global ratings. Interrater reliability, measured as a correlation for total examination scores, was .97. Mean scores on the examination were 60%, 64%, and 87% for PGY-1 family medicine, PGY-2 family medicine, and surgery residents, respectively. The difference in scores between family medicine and surgery residents was significant (p < .001), providing evidence of construct validity. CONCLUSION: A new examination developed for assessing family medicine residents' skills with minor surgical office procedures is reliable and has evidence for construct validity. The examination has low reliability when family physicians serve as examiners, but moderate reliability when surgeons are the 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 | 0.003 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".