Rates of Smoking Cessation at 6 and 12 Months after a Clinical Tobacco Smoking Cessation Intervention in Head and Neck Cancer Patients in Northern Ontario, Canada
Bibliographic record
Abstract
Smoking during cancer treatment is associated with reduced treatment response and cancer recurrence in patients with tobacco-related cancers. The purpose of this study was to examine smoking characteristics in head and neck cancer patients (n = 503) with a history of smoking and examine the impact of an intensive clinical tobacco intervention to patients who were currently smoking. All participants completed an interviewer-administered questionnaire at study enrollment which examined smoking behaviours, motivations to quit, and strategies used to cessate smoking. Follow-up assessments were completed at 6- and 12-months which monitored whether patients had quit smoking, remained cessated, or continued to smoke since study recruitment. For those who were currently smoking (n = 186, 37.0%), an intensive clinical tobacco intervention that utilized the 3A’s—Ask, Advise, Arrange—and the Opt-Out approach was offered to assist with smoking cessation at their new patient visit and followed-up weekly during their head and neck radiation therapy for 7 weeks. At 6 months, 23.7% (n = 41) of those who were smoking successfully quit; 51.2% quit ‘cold turkey’ (defined as using no smoking cessation assistance, aids or pharmacotherapy to quit), while 34.9% used pharmacotherapy (varenicline (Champix)) to quit. On average, it took those who were smoking 1–5 attempts to quit, but once they quit they remained cessated for the duration of the study. Although the head and neck cancer patients in this study reported high levels of nicotine dependence, many were able to successfully cessate.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".