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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".