Assessing cardiac physical examination skills using simulation technology and real patients: a comparison study
Bibliographic record
Abstract
OBJECTIVE: High-stakes assessments of doctors' physical examination skills often employ standardised patients (SPs) who lack physical abnormalities. Simulation technology provides additional opportunities to assess these skills by mimicking physical abnormalities. The current study examined the relationship between internists' cardiac physical examination competence as assessed with simulation technology compared with that assessed with real patients (RPs). METHODS: The cardiac physical examination skills and bedside diagnostic accuracy of 28 internists were assessed during an objective structured clinical examination (OSCE). The OSCE included 3 modalities of cardiac patients: RPs with cardiac abnormalities; SPs combined with computer-based, audio-video simulations of auscultatory abnormalities, and a cardiac patient simulator (CPS) manikin. Four cardiac diagnoses and their associated cardiac findings were matched across modalities. At each station, 2 examiners independently rated a participant's physical examination technique and global clinical competence. Two investigators separately scored diagnostic accuracy. RESULTS: Inter-rater reliability between examiners for global ratings (GRs) ranged from 0.75-0.78 for the different modalities. Although there was no significant difference between participants' mean GRs for each modality, the correlations between participants' performances on each modality were low to modest: RP versus SP, r = 0.19; RP versus CPS, r = 0.22; SP versus CPS, r = 0.57 (P < 0.01). CONCLUSIONS: Methodological limitations included variability between modalities in the components contributing to examiners' GRs, a paucity of objective outcome measures and restricted case sampling. No modality provided a clear 'gold standard' for the assessment of cardiac physical examination competence. These limitations need to be addressed before determining the optimal patient modality for high-stakes assessment purposes.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".