Physician judgement in predicting obstructive coronary artery disease and adverse events in chest pain patients
Bibliographic record
Abstract
OBJECTIVE: To evaluate informal physician judgement versus pretest probability scores in estimating risk in patients with suspected coronary artery disease (CAD). METHODS: We included 4533 patients from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial. Physicians categorised a priori the pretest probability of obstructive CAD (≥70% or ≥50% left main); Diamond-Forrester (D-F) and European Society of Cardiology (ESC) pretest probability estimates were calculated. Agreement was calculated using the κ statistic; logistic regression evaluated estimates of pretest CAD probability and actual CAD (as determined by CT coronary angiography), and clinical outcomes were modelled using Cox proportional hazard models. RESULTS: Physician estimates agreed poorly with D-F (κ 0.16; 95% CI 0.14 to 0.18) and ESC (κ 0.04; 95% CI 0.02 to 0.05). Actual obstructive CAD was significantly more prevalent in both the high-likelihood (OR 3.30; 95% CI 2.30 to 4.74) and the intermediate-likelihood (OR 1.43; 95% CI 1.16 to 1.76) physician-estimated groups versus the low-likelihood group; ESC similarly differentiated between the three groups (OR 9.07; 95% CI 2.87 to 28.70; and OR 3.87; 95% CI 1.22 to 12.28). However, using D-F, only the high-probability group differed (OR 2.49; 95% CI 1.74 to 3.54). Only physician estimates were associated with a higher incidence of adjusted death/myocardial infarction/unstable angina hospitalisation in the high-probability versus low-probability group (HR 2.68; 95% CI 1.52 to 4.74); neither pretest probability score provided prognostic information. CONCLUSIONS: Compared with D-F and ESC estimates, physician judgement more accurately identified obstructive CAD and worse patient outcomes. Integrating physician judgement may improve risk prediction for patients with stable chest pain. TRIAL REGISTRATION NUMBER: NCT01174550.
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 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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".