Abstract 18360: Predictors of Physician Under-recognition of Angina in Outpatients With Stable Coronary Artery Disease
Bibliographic record
Abstract
Background: Under-recognition of angina by physicians may result in under-treatment with revascularization or medications that could improve patients’ quality of life. Patient and physician characteristics associated with under-recognition have never been described. Methods: Outpatients with stable CAD in a 24-site US registry completed the Seattle Angina Questionnaire (SAQ) and their physicians independently quantified patients’ angina in the month prior to their clinic visit. Angina frequency was categorized as none, monthly, and daily/weekly. Among patients who reported angina, under-recognition was defined as the physician reporting a lower frequency of angina than the patient. A hierarchical (for site and physician) logistic model examined patient and physician factors associated with under-recognition of angina. Physician variability was assessed with a median odds ratio (MOR), which compares the likelihood of 1 physician at 1 random site under-recognizing angina vs. another physician at another site. Results: Among 1203 patients with stable CAD, 304 patients reported angina in the prior month, of whom 122 (40%) were under-recognized by their physician. Physicians were more likely to under-recognize the frequency of angina in patients with heart failure and among patients with less frequent angina (Figure). No other patient or physician factors were associated with under-recognition. There was significant variability across physicians (MOR 2.6), indicating that some physicians were better than others at recognizing angina. Conclusions: Under-recognition of angina is common in routine clinical practice and was largely unrelated to standard patient and physician characteristics. The large variation across physicians suggests that a more systematic approach is needed to assess angina from patients with CAD. The use of a validated tool, such as the SAQ, should be tested for improving angina recognition and outcomes.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".