Does Free nicotine Replacement Improve Smoking Cessation Rates in Cancer Patients?
Bibliographic record
Abstract
Background: Cigarette smoking is carcinogenic and has been linked to inferior treatment outcomes and complication rates in cancer patients. Here, we report the results of an 18-month pilot smoking cessation program that provided free nicotine replacement therapy (nrt). Methods: In January 2017, the smoking cessation program at our institution began offering free nrt for actively cigarette-smoking patients with cancer. The cost of 4 weeks of nrt was covered by the program, and follow-up was provided by smoking cessation champions. Results: From January 2017 to June 2018, 8095 patients with cancer were screened for cigarette use, of whom 1135 self-identified as current or recent smokers. Of those 1135 patients, 117 enrolled in the program and accepted a prescription for nrt. The rates of patient referral and patients attending a referral appointment were significantly higher in 2018–2018 than they had been in 2015–2016 (100% vs. 80.3%, p < 0.001, and 27.6% vs. 11.3%, p < 0.001, respectively). Median follow-up was 9.0 months (25%–75% interquartile range: 5.7–11.6 months). Of the patients who accepted nrt and who also had complete data (n = 71), 25 (35.2%) reported complete smoking cessation, and 32 (45.1%) reported only decreased cigarette smoking. On univariable analysis, no factors were significantly predictive of smoking cessation, although initial cigarette use (>10 vs. ≤10 initial cigarettes) was significantly predictive of smoking reduction (odds ratio: 5.04; 95% confidence interval: 1.46 to 17.45; p = 0.011). Conclusions: This pilot study of free nrt demonstrated rates of referral and acceptance of nrt that were improved compared with historical rates, and most referred patients either decreased their use of cigarettes or quit entirely.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".