Improved patient outcome with smoking cessation: when is it too late?
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
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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