Improved patient outcome with smoking cessation: when is it too late?
Bibliographic record
Abstract
Smoking is the leading modifiable risk factor for chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and lung cancer. Smoking cessation is the only proven way of modifying the natural course of COPD. It is also the most effective way of reducing the risk for myocardial infarction and lung cancer. However, the full benefits of tobacco treatment may not be realized until many years of abstinence. All patients with COPD, regardless of severity, appear to benefit from tobacco treatment. Similarly, patients with recent CVD events also benefit from tobacco treatment. The risk of total mortality and rate of recurrence of lung cancer is substantially lower in smokers who manage to quit smoking following the diagnosis of early stage lung cancer or small cell lung cancer. Together, these data suggest that tobacco treatment is effective both as a primary and a secondary intervention in reducing total morbidity and mortality related to COPD, CVD, and lung cancer. In this paper, we summarize the evidence for tobacco treatment and the methods by which smoking cessation can be promoted in smokers with lung disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".