Predictors of Adverse Smoking Outcomes in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
Bibliographic record
Abstract
BACKGROUND: The impact of lung cancer screening on smoking behavior is unclear. The aims of this ancillary study of the Prostate Lung Colorectal and Ovarian Cancer Screening Trial were to produce risk prediction models to identify individuals at risk of relapse or continued smoking and to evaluate whether cancer-screening variables affect long-term smoking outcomes. METHODS: Participants completed a baseline questionnaire at trial enrollment and a supplemental questionnaire 4-14 years after enrollment, which assessed several cancer-related variables, including family history of cancer, comorbidities, and tobacco use. Multivariable logistic regression models were used to predict smoking status at completion of the supplemental questionnaire. The models' predictive performances were evaluated by assessing discrimination via the receiver operator characteristic area under the curve (ROC AUC) and calibration. Models were internally validated using bootstrap methods. RESULTS: Of the 31 694 former smokers on the baseline questionnaire, 1042 (3.3%) had relapsed (ie, reported being a current smoker on the supplemental questionnaire). Of the 6807 current smokers on the baseline questionnaire, 4439 (65.2%) reported continued smoking on the supplemental questionnaire. Relapse was associated with multiple demographic, medical, and tobacco-related characteristics. This model had a bootstrap median ROC AUC of 0.862 (95% confidence interval [CI] = 0.858 to 0.866) and a calibration slope of 1.004 (95% CI = 0.978 to 1.029), indicating excellent discrimination and calibration. Predictors of continued smoking also included multiple demographic, medical, and tobacco-related characteristics. This model had an ROC AUC of 0.611 (95% CI = 0.605 to 0.614) and a slope of 1.006 (95% CI = 0.962 to 1.041), indicating modest discrimination. Neither the trial arm nor the lung-screening result was statistically significantly associated with smoking outcomes. CONCLUSION: These models, if validated externally, may have public health utility in identifying individuals at risk for adverse smoking outcomes, who may benefit from relapse prevention and smoking cessation interventions.
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 | 0.007 | 0.014 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".