Evaluating Smoking Cessation Interventions and Cessation Rates in Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background. Tobacco smoking cessation interventions in the oncology population are an important part of comprehensive treatment plan. Objectives. To evaluate through a systematic review smoking cessation interventions and cessation rates in cancer patients. Search Strategy. The literature was searched using Medline, EMBASE, and the Cochrane Library (inception to November 2010) by three independent review authors. Selection Criteria. Studies were included if tobacco smoking cessation interventions were evaluated and patients were randomized to usual care or an intervention. The primary outcome measure was cessation rates. Data Collection and Analysis. Two authors extracted data independently for each paper, with disagreements resolved by consensus. Main Results. The systematic review found eight RCTs investigating smoking cessation interventions in the oncology patient population. Pooled relative risks were calculated from two groups of RCTs of smoking cessation interventions based on followup duration. In both groups, the pooled relative risk did not suggest a statistically significant improvement in tobacco cessation compared to usual care. Conclusions. Our review demonstrates that recent interventions in the last decade which are a combination of non-pharmacological and pharmacological approaches yield a statistically significant improvement in smoking cessation rates compared to usual 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 | 0.022 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".