How Can We Better Help Cancer Patients Quit Smoking? The London Regional Cancer Program Experience with Smoking Cessation
Bibliographic record
Abstract
Background: Because continued cigarette smoking after a cancer diagnosis is associated with detrimental outcomes, supporting cancer patients with smoking cessation is imperative. We evaluated the effect of the Smoking Cessation Program at the London Regional Cancer Program (lrcp) over a 2-year period. Methods: The Smoking Cessation Program at the lrcp began in March 2014. New patients are screened for tobacco use. Tobacco users are counselled about the benefits of cessation and are offered referral to the program. If a patient accepts, a smoking cessation champion offers additional counselling. Follow-up is provided by interactive voice response (ivr) telephone system. Accrual data were collected monthly from January 2015 to December 2016 and were evaluated. Results: During 2015-2016, 10,341 patients were screened for tobacco use, and 18% identified themselves as current or recent tobacco users. In 2015, 84% of tobacco users were offered referral, but only 13% accepted, and 3% enrolled in ivr follow-up. At the lrcp in 2016, 77% of tobacco users were offered referral to the program, but only 9% of smokers accepted, and only 2% enrolled in ivr follow-up. Conclusions: The Smoking Cessation Program at the lrcp has had modest success, because multiple factors influence a patient's success with cessation. Limitations of the program include challenges in referral and counselling, limited access to nicotine replacement therapy (nrt), and minimal follow-up. To mitigate some of those challenges, a pilot project was launched in January 2017 in which patients receive free nrt and referral to the local health unit.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".