Effect of Patient-Specific Ratings vs Conventional Guidelines on Investigation Decisions in Angina
Bibliographic record
Abstract
BACKGROUND: Conventional guidelines have limited effect on changing physicians' test ordering. We sought to determine the effect of patient-specific ratings vs conventional guidelines on appropriate investigation of angina. METHODS: Randomized controlled trial of 145 physicians receiving patient-specific ratings (online prompt stating whether the specific vignette was considered appropriate or inappropriate for investigation, with access to detailed information on how the ratings were derived) and 147 physicians receiving conventional guidelines from the American Heart Association and the European Society of Cardiology. Physicians made recommendations on 12 Web-based patient vignettes before and on 12 vignettes after these interventions. The outcome was the proportion of appropriate investigative decisions as defined by 2 independent expert panels. RESULTS: Decisions for exercise electrocardiography were more appropriate with patient-specific ratings (819/1491 [55%]) compared with conventional guidelines (648/1488 [44%]) (odds ratio [OR], 1.57; 95% confidence interval [CI], 1.36-1.82). The effect was stronger for angiography (1274/1595 [80%] with patient-specific ratings compared with 1009/1576 [64%] with conventional guidelines [OR, 2.24; 95% CI, 1.90-2.62]). Within-arm comparisons confirmed that conventional guidelines had no effect but that patient-specific ratings significantly changed physicians' decisions toward appropriate recommendations for exercise electrocardiography (55% vs 42%; OR, 2.62; 95% CI, 2.14-3.22) and for angiography (80% vs 65%; OR, 2.10; 95% CI, 1.79-2.47). These effects were robust to physician specialty (cardiologists and general practitioners) and to vignette characteristics, including older age, female sex, and nonwhite race/ethnicity. CONCLUSION: Patient-specific ratings, unlike conventional guidelines, changed physician testing behavior and have the potential to reduce practice variations and to increase the appropriate use of investigation.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".