Bibliographic record
Abstract
Despite unequivocal evidence that smoking cessation is beneficial in terms of survival, there is at present no firm evidence that smoking cessation programs save lives. While they do increase quit rates, the collective evidence from randomized trials is inconclusive with respect to long-term survival. Withdrawal symptoms and the potential for harm when a subjects relapses after a prolonged period of cessation (e.g., 5+ years) might mitigate some or all of the benefits of the sustained quitters. This paper will review the key survival epidemiology and argue for a large randomized field trial of about 30,000 subjects, followed personally for 5 years and collectively for 15 years through the National Death Index. The intervention should be personalized, but reproducible through a treatment assignment algorithm. Personal coaching should be a major part of the intervention. Important short-term data on healthcare utilization should also be collected. Strong financial motivation for quitting (or prevention of smoking in the first place) is also presented. This paper is intended to motivate a large collective effort amongst the US Clinical and Translational Science Awardees to design the intervention and bring together the interested players to conduct the study.
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 | 0.034 | 0.043 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.015 |
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".