Systematic Reviews and Meta- and Pooled Analyses Quantitative Association of Tobacco Smoking With the Risk of Nasopharyngeal Carcinoma: AComprehensive Meta-Analysis of Studies Conducted Between
Bibliographic record
Abstract
Over the years, many studies have attempted to establish a link between tobacco smoking and an increased risk of nasopharyngeal carcinoma (NPC), but their results have been inconsistent. To clarify this link, we first conducted a comprehensive meta-analysis to integrate the findings of epidemiologic studies from the last half-century. The methodology used for this study followed the checklist proposed by the Meta-analysis of Observa-tional Studies in Epidemiology (MOOSE) Group. Pooled risk estimates were generated using a random-effects model. Twenty-eight case-control studies and 4 cohort studies involving a total of 10,274 NPC cases and 415,266 comparison subjects were included. A substantial effect of smoking on the risk of NPC was identified in this study. The results showed that ever smokers had a 60 % greater risk of developing the disease than never smokers (95% confidence interval: 1.38, 1.87); this was a robust dose-dependent association. More importantly, stronger associ-ations were observed in low-risk populations and among persons with the predominant histological type of differ-entiated NPC than in high-risk populations and persons with an undifferentiated type; the odds ratios were 1.76 and 2.20, respectively, versus 1.29 and 1.27. In this comprehensive meta-analysis, well-established statistical evi-dence was provided about the role of tobacco smoking in the etiology of NPC. case-control studies; cohort studies; meta-analysis; nasopharyngeal carcinoma; odds ratio; tobacco smoking Abbreviations: CI, confidence interval; NOS, Newcastle-Ottawa Scale; NPC, nasopharyngeal carcinoma; OR, odds ratio.
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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 | 0.039 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".