A Novel Approach to Assessing Technical Competence of Colorectal Surgery Residents
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate an objective method of technical skills assessment for graduating subspecialists in colorectal (CR) surgery-the Colorectal Objective Structured Assessment of Technical Skill (COSATS). BACKGROUND: It may be reasonable for the public to assume that surgeons certified as competent have had their technical skills assessed. However, technical skill, despite being the hallmark of a surgeon, is not directly assessed at the time of certification by surgical boards. METHODS: A procedure-based, multistation technical skills examination was developed to reflect a sample of the range of skills necessary for CR surgical practice. These consisted of bench, virtual reality, and cadaveric models. Reliability and construct validity were evaluated by comparing 10 graduating CR residents with 10 graduating general surgery (GS) residents from across North America. Expert CR surgeons, blinded to level of training, evaluated performance using a task-specific checklist and a global rating scale. The mean global rating score was used as the overall examination score and a passing score was set at "borderline competent for CR practice." RESULTS: The global rating scale demonstrated acceptable interstation reliability (0.69) for a homogeneous group of examinees. Both the overall checklist and global rating scores effectively discriminated between CR and GS residents (P < 0.01), with 27% of the variance attributed to level of training. Nine CR residents but only 3 GS residents were deemed competent. CONCLUSIONS: The Colorectal Objective Structured Assessment of Technical Skill effectively discriminated between CR and GS residents. With further validation, the Colorectal Objective Structured Assessment of Technical Skill could be incorporated into the colorectal board examination where it would be the first attempt of a surgical specialty to formally assess technical skill at the time of 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".