Reporting of tobacco use and tobacco-related analyses in cancer cooperative group clinical trials: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Continued smoking after a diagnosis of cancer negatively impacts cancer outcomes, but the impact of tobacco on newer treatments options is not well established. Collecting and evaluating tobacco use in clinical trials may advance understanding of the consequences of tobacco use on treatment modalities, but little is known about the frequency of reporting and analysis of tobacco use in cancer cooperative clinical trial groups. PATIENTS AND METHODS: A comprehensive literature search was conducted to identify cancer cooperative group clinical trials published from January 2017-October 2019. Eligible studies evaluated either systemic and/or radiation therapies, included ≥100 adult patients, and reported on at least one of: overall survival, disease/progression-free survival, response rates, toxicities/adverse events, or quality-of-life. RESULTS: A total of 91 studies representing 90 trials met inclusion criteria with trial start dates ranging from 1995 to 2015 with 14% involving lung and 5% head and neck cancer patients. A total of 19 studies reported baseline tobacco use; 2 reported collecting follow-up tobacco use. Seven studies reported analysis of the impact of baseline tobacco use on clinical outcomes. There was significant heterogeneity in the reporting of baseline tobacco use: 7 reported never/ever status, 10 reported never/ex-smoker/current smoker status, and 4 reported measuring smoking intensity. None reported verifying smoking status or second-hand smoke exposure. Trials of lung and head and neck cancers were more likely to report baseline tobacco use than other disease sites (83% versus 6%, P < 0.001). CONCLUSIONS: Few cancer cooperative group clinical trials report and analyze trial participants' tobacco use. Significant heterogeneity exists in reporting tobacco use. Routine standardized collection and reporting of tobacco use at baseline and follow-up in clinical trials should be implemented to enable investigators to evaluate the impact of tobacco use on new cancer therapies.
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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 | 0.110 | 0.422 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".