Assessing the Relationship between Cardiac Physical Examination Technique and Accurate Bedside Diagnosis during an Objective Structured Clinical Examination (OSCE)
Bibliographic record
Abstract
BACKGROUND: Many standardized patient (SP) encounters employ SPs without physical findings and, thus, assess physical examination technique. The relationship between technique, accurate bedside diagnosis, and global competence in physical examination remains unclear. METHOD: Twenty-eight internists undertook a cardiac physical examination objective structured clinical examination, using three modalities: real cardiac patients (RP), "normal" SPs combined with related cardiac audio-video simulations, and a cardiology patient simulator (CPS). Two examiners assessed physical examination technique and global bedside competence. Accuracy of cardiac diagnosis was scored separately. RESULTS: The correlation coefficients between participants' physical examination technique and diagnostic accuracy were 0.39 for RP (P < .05), 0.29 for SP, and 0.30 for CPS. Patient modality impacted the relative weighting of technique and diagnostic accuracy in the determination of global competence. CONCLUSIONS: Assessments of physical examination competence should evaluate both technique and diagnostic accuracy. Patient modality affects the relative contributions of each outcome towards a global rating.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".