Smoking status and survival: impact on mortality of continuing to smoke one year after the angiographic diagnosis of coronary artery disease, a prospective cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Smoking is an undertreated risk factor for coronary artery disease (CAD) and is associated with adverse outcomes after myocardial infarction. Aims of our study were to determine if management of CAD by medical therapy (MT) alone or with coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI) influence smoking status at one year following angiography and if a change in smoking status at one year influences long term survival. METHODS: Prospective cohort study using the APPROACH registry. Two cohorts were examined: (1) 11,334 patients who returned a one year follow-up questionnaire; (2) 4,246 patients propensity-matched based on their post-angiography treatment - MT or revascularization (RV). Multivariate modeling and survival analysis were used. RESULTS: In the propensity-matched cohort, quit rates at one year were greater among CABG patients (68%) than PCI (37%) or MT patients (47%). Smokers in the RV group, who self-reported quitting at one year, had a significantly reduced mortality compared to those who continued to smoke. CONCLUSIONS: CABG patients were more likely to quit smoking than those treated with MT alone or PCI. Quitting smoking was associated with improved long-term survival; smoking remains a key risk factor for mortality in patients with CAD. These data underscore the importance of nicotine addiction management in patients with CAD and the need to emphasize cessation particularly in those patients undergoing MT or PCI.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it