Abstract 12914: Impact of Angina Frequency on the Health-Related Quality-of-Life of Patients With Chronic Angina
Bibliographic record
Abstract
Background: Chronic angina is a highly symptomatic disease that can have a profound impact on patients’ health-related quality-of-life (HrQoL). The aim of this study was to quantify the relationship between angina frequency and HrQoL. Methods: This was a post-hoc analysis from the 6-week double-blind treatment phase of the Efficacy of Ranolazine in Chronic Angina (ERICA) trial which evaluated 565 patients with stable coronary disease reporting ≥3 angina attacks/week. Angina frequency was classified using the Seattle Angina Questionnaire angina frequency (SAQAF) domain (scores of 100=no; 61-99=monthly; 31-60=weekly; 0-30=daily angina symptoms). HrQoL was assessed based on EuroQol (EQ)-5D scores derived using individual patient data from the ERICA trial and a previously published mapping equation. Median (25%, 75% range) EQ-5D scores for each angina frequency classification at the end of the 6-week period were calculated. Additionally, changes in EQ-5D scores from baseline to end-of-trial for patients achieving and not achieving a ≥20 point improvement in SAQAF score (previously reported as a minimally important improvement) were compared. Results: Both SAQAF and EQ-5D scores were available in 548 patients (97% of all randomized) (Table). The total population reported a median EQ-5D score of 0.68 (0.62, 0.77). Compared to patients reporting no angina symptoms, patients reporting monthly, weekly and daily angina symptoms had significantly poorer HrQoL (p≤0.001 for all). Patients who improved ≥20 points on the SAQAF from baseline (n=369, 67%) experienced a median 0.08 greater improvement in EQ-5D score compared to those not achieving a ≥20 point improvement (p≤0.001). Conclusions: Among patients suffering from chronic angina, HrQoL decreases as angina frequency increases. Patients reporting at least a minimally important improvement in angina frequency experience a tangible improvement in HrQoL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".