Effect of angina under-recognition on treatment in outpatients with stable ischaemic heart disease
Bibliographic record
Abstract
AIMS: Almost a third of outpatients with chronic coronary artery disease (CAD) report having angina in the prior month, which is frequently under-recognized by their cardiologists. Whether under-recognition is associated with less treatment escalation to control angina, and potential underuse of treatment, is unknown. METHODS AND RESULTS: Patients with CAD from 25 US cardiology outpatient practices completed the Seattle Angina Questionnaire (SAQ) prior to their clinic visit, and angina was categorized as daily, weekly, monthly and no angina. Cardiologists (n=155) independently quantified patients' angina, blinded to patients' SAQ scores. Under-recognition was defined as the physician reporting a lower category of angina frequency than the patient. Among 1257 patients with CAD, 411 reported angina in the past month, of whom 178 (43.3%) patients were under-recognized. Treatment escalation-defined as intensification (up-titration or addition) of antianginal medications, referral for diagnostic testing or revascularization, or hospital admission-occurred in 106 (25.8%) patients with angina. Patients with under-recognized angina were less likely to get treatment escalation than patients whose angina was appropriately recognized (8.4% vs 39.1%, P<0.001). In a hierarchical multivariable logistic regression model adjusting for demographic and clinical characteristics, as well as the burden of angina, under-recognition remained strongly associated with a lack of treatment escalation (adjusted OR 0.10, 95% CI 0.04-0.21, P<0.001). CONCLUSIONS: Under-recognition of angina in cardiology outpatient practices is associated with less aggressive treatment escalation and may lead to poorer angina control. Standardizing clinical recognition of angina using validated tools could reduce under-recognition of angina, facilitate treatment, and potentially improve 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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".