The impact of tobacco retail density on overall survival (OS) in lung cancer survivors.
Bibliographic record
Abstract
12058 Background: Continued smoking after a cancer diagnosis is associated with poorer outcomes. We previously identified that tobacco retail outlet density is negatively associated with cessation in lung cancer survivors (ASCO 2019). However, the impact of tobacco retail density on survival has not been evaluated. We evaluated the impact of tobacco retail density on OS in lung cancer patients (pts). Methods: Lung cancer pts diagnosed from 2009-2012 were recruited at diagnosis and completed a baseline questionnaire on their socio-demographics, ECOG and smoking history. Clinicopathologic data including stage, histology and OS data were collected. Validated tobacco retail location data obtained from Ministry of Health and pt home addresses were geocoded using ArcGIS 10.6.1, which calculated tobacco outlet density within 250 meters (m) and 500m from pts. Multivariable Cox proportional hazard models evaluated the impact of tobacco outlet density on OS adjusted for significant clinicodemographic covariates. Results: Among 1411 pts, median age 66, 53% female, 8% small cell/56% adenocarcinoma/17% squamous/19% other, 28% stage 1/9% stage 2/20% stage 3/35% stage 4, 38% were current smokers at diagnosis and 40% were ex-smokers; median OS was 24 months. On average, there was one vendor (range 0-23) within 250m and four vendors (range 0-44) within 500m from pts; 33% and 60% of pts lived within 250m and 500m from at least one vendor respectively. The final baseline multivariable model consisted of age, gender, stage, smoking status, ECOG and neighbourhood marginalization index ( P< 0.05). Among all pts, not living within 250m to an outlet improved OS (aHR 0.84 [0.72-0.97] P= 0.02). Living near more outlets within 250 m (aHR 1.03 per outlet [1.00-1.05] P= 0.03) or 500 m (aHR 1.01 per outlet [1.00-1.02] P= 0.04) worsened OS. Subgroup analysis based on smoking status at diagnosis, identified that among current smokers, not living within 250m to an outlet improved OS (aHR 0.76 [0.60-0.97] P= 0.03), and among ex-smokers, living near more outlets within 500 m worsened OS (aHR 1.02 per outlet [0.99-1.03] P= 0.07); other associations showed similar directionality. Among 135 current smokers at diagnosis with follow-up smoking status, not living within 250m to an outlet continued to show a trend towards improved OS (aHR 0.57 [0.31-1.03] P= 0.06), after also adjusting for follow-up smoking status. Conclusions: Living near a greater density of tobacco outlets is associated with poorer OS among lung cancer pts. Reducing the density of tobacco outlets may be a strategy that can help improve lung 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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".