Does exposure to simulated patient cases improve accuracy of clinicians’ predictive value estimates of diagnostic test results? A within-subjects experiment at St Michael’s Hospital, Toronto, Canada
Bibliographic record
Abstract
Objective Clinicians often overestimate the probability of a disease given a positive test result (positive predictive value; PPV) and the probability of no disease given a negative test result (negative predictive value; NPV). The purpose of this study was to investigate whether experiencing simulated patient cases (ie, an ‘experience format’) would promote more accurate PPV and NPV estimates compared with a numerical format. Design Participants were presented with information about three diagnostic tests for the same fictitious disease and were asked to estimate the PPV and NPV of each test. Tests varied with respect to sensitivity and specificity. Information about each test was presented once in the numerical format and once in the experience format. The study used a 2 (format: numerical vs experience) × 3 (diagnostic test: gold standard vs low sensitivity vs low specificity) within-subjects design. Setting The study was completed online, via Qualtrics (Provo, Utah, USA). Participants 50 physicians (12 clinicians and 38 residents) from the Department of Family and Community Medicine at St Michael’s Hospital in Toronto, Canada, completed the study. All participants had completed at least 1 year of residency. Results Estimation accuracy was quantified by the mean absolute error (MAE; absolute difference between estimate and true predictive value). PPV estimation errors were larger in the numerical format (MAE=32.6%, 95% CI 26.8% to 38.4%) compared with the experience format (MAE=15.9%, 95% CI 11.8% to 20.0%, d =0.697, P<0.001). Likewise, NPV estimation errors were larger in the numerical format (MAE=24.4%, 95% CI 14.5% to 34.3%) than in the experience format (MAE=11.0%, 95% CI 6.5% to 15.5%, d =0.303, P=0.015). Conclusions Exposure to simulated patient cases promotes accurate estimation of predictive values in clinicians. This finding carries implications for diagnostic training and practice.
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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 | 0.022 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".