Smoking Cessation Interventions and Cessation Rates in the Oncology Population: An Updated Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate tobacco smoking cessation interventions and cessation rates in the oncology population through a systematic review and meta-analysis. DATA SOURCES: The literature was searched using PubMed, Google Scholar, Medline, EMBASE, and the Cochrane Library (inception to October 2012) by 3 independent review authors. REVIEW METHODS: Studies were included if they were randomized controlled trials (RCTs) or prospective cohort (PCs) studies evaluating tobacco smoking cessation interventions with patients assigned to a usual care or an intervention group. The primary outcome measure was smoking cessation rates. Two authors extracted data independently for each study. When applicable, disagreements were resolved by consensus. RESULTS: The systematic review identified 10 RCTs and 3 PCs. Statistical analysis was conducted using StatsDirect software (Cheshire, UK). Pooled odds ratios (ORs) for smoking cessation interventions were calculated in 2 groups based on follow-up duration. The therapeutic interventions included counseling, nicotine replacement therapy, buproprion, and varenicline. Smoking cessation interventions had a pooled odds ratio of 1.54 (95% confidence interval [CI], 0.909-2.64) for patients in the shorter follow-up group and 1.31 (95% CI, 0.931-1.84) in the longer follow-up group. Smoking cessation interventions in the perioperative period had a pooled odds ratio of 2.31 (95% CI, 1.32-4.07). CONCLUSION: Our systematic review and meta-analysis demonstrate that tobacco cessation interventions in the oncology population, in both the short-term and long-term follow-up groups, do not significantly affect cessation rates. The perioperative period, though, may represent an important teachable moment with regard to smoking cessation.
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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.043 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".