Development and Validation of a Cardiac Findings Checklist for Use With Simulator-Based Assessments of Cardiac Physical Examination Competence
Bibliographic record
Abstract
INTRODUCTION: Objective outcome measures for use with simulator-based assessments of cardiac physical examination competence are lacking. The current study describes the development and validation of an approach to scoring performance using a cardiac findings checklist. METHODS: A cardiac findings checklist was developed and implemented for use with a simulator-based assessment of cardiac physical examination competence at a Canadian national specialty examination in internal medicine. Candidate performance as measured using the checklist was compared with global ratings of clinical performance on the cardiac patient simulator and with overall examination performance. RESULTS: Interrater reliability for scoring the checklist ranged from 0.95 for scoring correct findings to 0.72 for scoring incorrect findings. A summary checklist score had a Pearson correlation of 0.60 with overall candidate performance on the simulator-based station. CONCLUSION: Use of a cardiac findings checklist provides one objective measure of cardiac physical examination competence that may be used with simulator-based assessments.
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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 | 0.052 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".