Association of Smoking Status With Angina and Health-Related Quality of Life After Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Smoking cessation after acute myocardial infarction (AMI) decreases the risk of recurrent AMI and mortality by 30% to 50%, but many patients continue to smoke. The association of smoking with angina and health-related quality of life (HRQOL) after AMI is unclear. METHODS AND RESULTS: Patients in 2 US multicenter AMI registries (n=4003) were assessed for smoking and HRQOL at admission and 1, 6, and 12 months after AMI. Angina and HRQOL were measured with the Seattle Angina Questionnaire and Short Form-12 Physical and Mental Component Scales. At admission, 29% never had smoked, 34% were former smokers (quit before AMI), and 37% were active smokers, of whom 46% quit by 1 year (recent quitters). In hierarchical, multivariable, regression models that adjusted for sociodemographic, clinical and treatment factors, never and former smokers had similar and the best HRQOL in all domains. Recent quitters had intermediate HRQOL levels, with angina and Short Form-12 Mental Component Scale scores similar to never smokers. Persistent smokers had worse HRQOL in all domains compared with never smokers and worse Short Form-12 Mental Component Scale scores than recent quitters. CONCLUSIONS: Smoking after AMI is associated with more angina and worse HRQOL in all domains, whereas smokers who quit after AMI have similar angina levels and mental health as never smokers. These observations may help encourage patients to stop smoking after AMI.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".