Hot Seat Diagnosis
Bibliographic record
Abstract
CONTEXT.—: Competency-based medical education relies on frequent formative in-service assessments to ascertain trainee progression. Currently at our institution, trainees receive a summative end-of-rotation In-Training Evaluation Report based on feedback collected from staff pathologists. There is no method of simulating report sign-out. OBJECTIVE.—: To develop a formative in-service assessment tool that is able to simulate report sign-out and provide case-by-case feedback to trainees. Further, to compare time- versus competency-based assessment models. DESIGN.—: Twenty-one pathology trainees were assessed for 20 months. Hot Seat Diagnosis by trainees and trainee assessment by pathologists were recorded in the laboratory information system. In the first iteration, trainees were assessed by using a time-based assessment scale on their ability to diagnose, report, use ancillary tests, comment on clinical implications, and provide intraoperative consultation and/or gross cases. The second iteration used a competency-based assessment scale. Trainees and pathologists completed surveys on the effectiveness of the In-Training Evaluation Report versus the Hot Seat Diagnosis tool. RESULTS.—: Scores from both iterations correlated significantly with other assessment tools including the Resident In-Service Examination (r = 0.93, P = .04 and r = 0.87, P = .03). The competency-based model was better able to demonstrate improvement over time and stratify junior versus senior trainees than the time-based model. Trainees and pathologists rated Hot Seat Diagnosis as significantly more objective, detailed, and timely than the In-Training Evaluation Report, and effective at simulating report sign-out. CONCLUSIONS.—: Hot Seat Diagnosis is an effective tool for the formative in-service assessment of pathology trainees and simulation of report sign-out, with the competency-based model outperforming the time-based model.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".