Cigarette-Smoking Characteristics and Interest in Cessation in Patients with Head-and-Neck Cancer
Bibliographic record
Abstract
Purpose: Many patients diagnosed with head-and-neck cancer are current or former smokers. Despite the well-known adverse effects of smoking, continuation of smoking during cancer treatment is associated with reduced efficacy of that treatment and with cancer recurrence. In the present study, we examined smoking characteristics in patients with head-and-neck cancer near the time of cancer treatment. Methods: A prospective cohort of patients with head-and-neck cancer who attended a dental oncology clinic before receiving cancer treatment at a regional cancer centre were invited to participate in a study that involved completing an interviewer-administered questionnaire to assess smoking characteristics, intention to quit, motivation to quit, and strategies perceived to potentially aid in successful cessation. Results: = 155) indicating a time to first cigarette of 30 minutes or less. Most had previously attempted to quit smoking (77.0%), and many had prior unsuccessful quit attempts before resuming smoking again. Most were interested in quitting smoking (85.8%), and many (70.5%) were seriously considering quitting smoking within the subsequent 30 days. Conclusions: Patients with head-and-neck cancer reported high nicotine dependence and high interest in cessation opportunities near the time of treatment for cancer. Those results might provide support for provision of smoking cessation opportunities.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".