Tobacco retail availability and tobacco cessation in lung and head and neck (HN) cancer survivors.
Bibliographic record
Abstract
11559 Background: Continued smoking after a cancer diagnosis is associated with poorer outcomes. Tobacco retail availability is negatively associated with cessation in non-cancer patients (pts), but has not been explored in cancer survivors. We evaluated the impact of tobacco retail availability on cessation in lung and HN cancer pts. Methods: Lung and HN cancer pts (Princess Margaret Cancer Centre, Toronto) completed questionnaires evaluating changes in tobacco use with a median of 26 months apart. Validated tobacco retail location data were obtained from Ministry of Health and pt home addresses were geocoded using ArcGIS 10.6.1, which calculated walking time/distance to nearest vendor, and vendor density within 250 meters (m) and 500m from pts. Multivariable logistic regression and Cox proportional hazard models evaluated the impact of vendor availability on cessation and time to quitting after diagnosis respectively, adjusting for significant clinico-demographic and tobacco covariates. Results: 242/721 lung and 149/445 HN pts smoked at diagnosis; subsequent overall quit rates were 66% and 49% respectively. Mean distance and walking time to a vendor was 1 km (range 0-13) and 11 min (range 0-156). On average, there was one vendor (range 0-19) within 250m and four vendors (range 0-40) within 500m from pts; 37% and 61% of pts lived within 250m and 500m from at least one vendor respectively. Greater distance (aOR 1.18 per 1000m [95% CI 1.00-1.38] p = 0.05) and increased walking time (aOR 1.01 per minute [1.00-1.02] p = 0.05) were associated with quitting at one year. Living within 250m (aOR 0.52 [0.32-0.84] p = 0.008) or 500m (aOR 0.57 [0.35-0.92] p = 0.02) to at least one vendor reduced quitting at one year. Living near more vendors within 500m had an increasing dose effect on reducing cessation rates at one year (aOR 0.96 per vendor [0.93-1.00] p = 0.05). Living within 500m to a vendor reduced chance of quitting at any time (aHR 0.66 [0.48-0.91] p = 0.01). HN and lung subgroups revealed similar associations. Conclusions: Close access to tobacco retail outlets is associated with reduced cessation rates for lung and HN cancer survivors. Reducing density of tobacco vendors is a cessation strategy that can positively impact cancer pt 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".