54. Assessing cardiac physical examination competence using simulation technology and real patientss
Bibliographic record
Abstract
Assessment of residents’ physical examination skills often involves the use of standardized patients lacking physical abnormalities. Simulation technology offers the potential benefit of mimicking physical abnormalities. The current study was undertaken to examine the relationship between physicians’ competence in cardiac physical examination as assessed using simulation technology compared to real patients. An OSCE was created using 3 modalities of cardiac patients: real patients (RP) with cardiac abnormalities, standardized patients (SP) combined with a computer-based audio-video simulation of auscultatory abnormalities and a cardiopulmonary patient simulator (CPS). The same four cardiac diagnoses were tested with each modality. Participants were 28 internists, within 3 years of passing the Royal College of Physicians and Surgeons of Canada’s (RCPSC) Comprehensive Examination in Internal Medicine. At each station, two RCPSC examiners independently rated a participant’s physical examination technique and provided a global rating of clinical competence. The accuracy of a participant’s cardiac diagnosis for each patient was scored separately by two investigators. The inter-rater reliability between examiners, for the global rating outcome, was 0.76 for RP stations, 0.78 for SP stations and 0.75 for CPS stations. The correlations between participants’ global ratings on each modality were: RP vs. SP, r=0.19; RP vs. CPS, r=0.22; SP vs. CPS, r=0.57 (p < 0.01). A number of methodological limitations were highlighted during the study, including difficulties in truly matching patients within and between modalities, differential weighting of components into the examiners’ global ratings based on modality and limitations of case specificity. No modality provided a clear “gold standard” to assess residents’ cardiac physical examination competence. In the context of assessment, until these limitations are addressed, simulation modalities may not be directly interchangeable with real patients. Boulet JR, Swanson DB. Psychometric challenges of using simulations for high-stakes assessment. In: Dunn WF (ed). Simulators in critical care education and beyond. Des Plaines, IL: Society of Critical Care Medicine 2004; 119-30. Hatala R, Kassen BO, Nishikawa J, Cole G, Issenberg SB. Incorporating simulation technology in a Canadian national specialty examination: a descriptive report. Academic Medicine. 2005; 80(6):554-6. Issenberg SB, McGaghie WC, Petrusa ER, Gordon DL, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2005; 27(1):10-28.
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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 | 0.004 | 0.016 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".