The Tobacco Endgame—A New Paradigm for Smoking Cessation in Cancer Clinics
Bibliographic record
Abstract
Smoking cessation represents an untapped resource for cancer therapy. Many people who smoke and have cancer (tobacco-related or otherwise) struggle to quit and as a result, jeopardise response to treatment, recovery after surgery and long-term survival. Many health care practitioners working in cancer medicine feel undertrained, unprepared and unsupported to provide effective smoking cessation therapy. Many institutions and healthcare systems do provide smoking cessation programs, guidelines and referral pathways for cancer patients, but these may be unevenly applied. The growing body of evidence, from both retrospective and prospective clinical studies, confirms the benefit of smoking cessation and will provide much needed evidence for the best and most effective interventions in cancer clinics. In addition to reducing demand, helping cancer patients quit and treating addiction, a firm commitment to developing smoke free societies may transform cancer medicine in the future. While the Framework Convention for Tobacco Control (FCTC) has dominated global tobacco control for the last two decades, many jurisdictions are starting to develop plans to make their communities tobacco free, to introduce the tobacco endgame. Characterised by downward pressure on tobacco supply, limited sales, limited access and denormalization of smoking, these policies may radically change the milieu in which people with cancer receive treatment, in which health care practitioners refine skills and which may ultimately foster dramatic improvements in cancer outcomes.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".