Practice Indicators of Suboptimal Care and Avoidable Adverse Events
Bibliographic record
Abstract
PURPOSE: To (1) compile an initial list of physician-related practice indicators (PRINDs) that contribute to causing or preventing suboptimal care (SOCR) and adverse events (AEs) and (2) determine the extent to which one national exam assessed these PRINDs. METHOD: In 2009-2010, the authors searched the literature and surveyed 17 physician experts to compile a list of PRINDs of SOCR and avoidable AEs. They then conducted a content analysis of the 2008 and 2009 Medical Council of Canada (MCC) Qualifying Examinations (QEs). RESULTS: The authors identified 92 unique PRINDs, of which 59 were behaviors or decisions expected of all physicians and suitable for assessment on a general medical examination. Of these, 36 (61%) were tested on the 2008 and 2009 MCC QEs. The mean number of PRINDs tested per exam was highest for Part I Knowledge (32.2), followed by Part I clinical decision making (CDM) (18.4) and Part II clinical performance (objective structured clinical examination [OSCE]) (9.8). The percentage of questions or cases per exam testing a PRIND (e.g., 14/36 [39%] for CDM and 5.26/12 [44%] for OSCE) differed from the percentage of the total test score attributed to PRINDs (e.g., 10.8/36 [30%] for CDM and 68.5/1,522.3 [5%] for OSCE). CONCLUSIONS: PRINDs represent candidates' abilities to avoid SOCR and AEs and constitute an important aspect of medical practice to be assessed on licensing or certifying examinations to best protect the public. The different scoring methods used to measure such knowledge and skills warrant further consideration.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".