Systematic Review of Tobacco Use after Lung or Head/Neck Cancer Diagnosis: Results and Recommendations for Future Research
Bibliographic record
Abstract
Tobacco use after cancer diagnosis is associated with adverse cancer outcomes, yet reliable prevalence estimates for this behavior are lacking. We conducted a systematic literature review of the prevalence of current tobacco use among individuals with a history of lung or head/neck cancer (CRD #42012002625). An extensive search of electronic databases (MEDLINE, EMBASE, Cochrane Library, CINAHL, PsycINFO, and Web of Science) identified 7,777 potentially relevant articles published between 1980 and 2014 and 131 of these yielded pertinent information. Aggregating results across heterogeneous study designs and diverse patient samples, the overall mean prevalence rate of current tobacco use (mostly cigarette smoking) was 33.0% (median, 31.0%). Among current tobacco users at cancer diagnosis, the mean prevalence rate of current tobacco use (mostly cigarette smoking) was 53.8% (median, 50.3%). In many cases, an operational definition of "current" tobacco use was absent, and biochemical verification of self-reported smoking status was infrequent. These and other observed methodologic limitations in the assessment and reporting of cancer patients' tobacco use underscore the necessity of uniform tobacco use assessment in future clinical research and cancer care.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".