Reduction of head and neck cancer risk following smoking cessation: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Head and neck (HN) cancer comprises the neoplasms originating from the oral cavity, pharynx and larynx. We aimed at reviewing the available literature on the effect of smoking cessation on HN cancer risk. METHOD: We conducted a systematic search in Medline, PubMed and Embase to June 2022. We abstracted or calculated relative risks (RR) and 95% CIs of HN cancer after cessation of tobacco smoking (both former smoking status and duration of quitting) and combined them using random effects meta-analyses. Papers included were case-control or cohort studies available in the English language. Studies investigating smoking cessation after cancer diagnosis, case reports, intervention studies or animal studies were excluded. Quality and susceptibility to bias of each included study were evaluated using the Newcastle-Ottawa Scale. Publication bias was assessed using funnel plot and Egger's test. RESULTS: A total of 65 studies were included in the review, including 5 cohort and 60 case-control studies. The RR of HN cancer for former smokers compared with current smokers was 0.40 (95% CI 0.35 to 0.46). In an analysis by cancer site, the RR of oral cancer was 0.44 (95% CI 0.35 to 0.55), that of pharyngeal cancer 0.44 (95% CI 0.32 to 0.60) and that of laryngeal cancer 0.38 (95% CI 0.29 to 0.50). The dose-response meta-analysis was based on 37 studies. The RR per 10-year increase in smoking cessation was 0.47 (95% CI 0.43 to 0.52). CONCLUSIONS: The risk of HN cancer declines within the first 5 years of quitting smoking. Quitting smoking is an essential element of HN cancer prevention. TRIAL REGISTRATION NUMBER: The protocol has been deposited in the PROSPERO repository (CRD42022338262).
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".