Implementing and Evaluating a National Certification Technical Skills Examination
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it