Implementing and Evaluating a National Certification Technical Skills Examination
Bibliographic record
Abstract
OBJECTIVE: To implement the Colorectal Objective Structured Assessment of Technical skill (COSATS) into American Board of Colon and Rectal Surgery (ABCRS) certification and build evidence of validity for the interpretation of the scores of this high stakes assessment tool. BACKGROUND DATA: Currently, technical skill assessment is not a formal component of board certification. With the technical demands of surgical specialties, documenting competence in technical skill at the time of certification with a valid tool is ideal. METHODS: In September 2014, the COSATS was a mandatory component of ABCRS certification. Seventy candidates took the examination, with their performance evaluated by expert colorectal surgeons using a task-specific checklist, global rating scale, and overall performance scale. Passing scores were set and compared using 2 standard setting methodologies, using a compensatory and conjunctive model. Inter-rater reliability and the reliability of the pass/fail decision were calculated using Cronbach alpha and Subkoviak methodology, respectively. Overall COSATS scores and pass/fail status were compared with results on the ABCRS oral examination. RESULTS: The pass rate ranged from 85.7% to 90%. Inter-rater reliability (0.85) and reliability of the pass/fail decision (0.87 and 0.84) were high. A low positive correlation (r= 0.25) was seen between the COSATS and oral examination. All individuals who failed the COSATS passed the ABCRS oral examination. CONCLUSIONS: COSATS is the first technical skill examination used in national surgical board certification. This study suggests that the current certification process may be failing to identify individuals who have demonstrated technical deficiencies on this standardized assessment tool.
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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 | 0.035 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".