Effect of Smoking Cessation on Mortality After Myocardial Infarction
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
OBJECTIVE: To determine the effect of smoking cessation on mortality after myocardial infarction. DATA SOURCES: English- and non-English-language articles published from 1966 through 1996 retrieved using keyword searches of MEDLINE and EMBASE supplemented by letters to authors and searching bibliographies of reviews. STUDY SELECTION: Selection of relevant abstracts and articles was performed by 2 independent reviewers. Articles were chosen that reported the results of cohort studies examining mortality in patients who quit vs continued smoking after myocardial infarction. DATA EXTRACTION: Mortality data were extracted from the selected articles by 2 independent reviewers. DATA SYNTHESIS: Twelve studies were included containing data on 5878 patients. The studies took place in 6 countries between 1949 and 1988. Duration of follow-up ranged from 2 to 10 years. All studies showed a mortality benefit associated with smoking cessation. The combined odds ratio based on a random effects model for death after myocardial infarction in those who quit smoking was 0.54 (95% confidence interval, 0.46-0.62). Relative risk reductions across studies ranged from 15% to 61%. The number needed to quit smoking to save 1 life is 13 assuming a mortality rate of 20% in continuing smokers. The mortality benefit was consistent regardless of sex, duration of follow-up, study site, and time period. CONCLUSION: Results of several cohort studies suggest that smoking cessation after myocardial infarction is associated with a significant decrease in mortality.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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