Does physical examination competence correlate with bedside diagnostic acumen? An observational study
Bibliographic record
Abstract
AIM: To examine the relationship between a physician's ability to examine a standardized patient (SP) and their ability to correctly identify related clinical findings created with simulation technology. METHOD: The authors conducted an observational study of 347 candidates during a Canadian national specialty examination at the end of post-graduate internal medicine training. Stations were created that combined physical examination of an SP with evaluation of a related audio-video simulation of a patient abnormality, in the domains of cardiology and neurology. Examiners evaluated a candidate's competence at performing a physical examination of an SP and their accuracy in diagnosing a related audio-video simulation. RESULTS: For the cardiology stations, the correlation between the physical examination scores and recognition of simulation abnormalities was 0.31 (p < 0.01). For the neurology stations, the correlation was 0.27 (p < 0.01). Addition of the simulations identified 18% of 197 passing candidates on the cardiology stations and 17% of 240 passing candidates on the neurology stations who were competent in their physical examination technique but did not achieve the passing score for diagnostic skills. CONCLUSIONS: Assessments incorporating SPs without physical findings may need to include other methodologies to assess bedside diagnostic acumen.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".