Association Between Alcohol Consumption and Risk of Nasopharyngeal Carcinoma: A Comprehensive Meta‐Analysis of Epidemiological Studies
Bibliographic record
Abstract
BACKGROUND: Alcohol consumption is increasing all over the world, but whether it is an independent factor affecting the occurrence of nasopharyngeal carcinoma (NPC) is inconsistent in many studies. We aimed to explore the association between alcohol consumption and NPC risk by integrating existing evidence in a meta-analysis. METHODS: We searched for relevant articles published up to August 2018 in PubMed, Cochrane Library, Web of Science, and China National Knowledge infrastructure (CNKI). The Newcastle-Ottawa scale was used to assess the quality of the included studies. Odds ratios (ORs) or relative risks were pooled to estimate the associations between alcohol consumption and NPC risk. RESULTS: The meta-analysis of cohort studies showed no significant association between alcohol consumption and NPC, but pooled results from case-control studies indicated that ever drinking increased the probability of NPC versus nondrinking (OR = 1.10; 95% confidence interval [CI]: 1.01, 1.19). As compared with nondrinkers, high-frequency drinking (≥7 times/wk) increased the NPC probability (OR = 1.29; 95% CI: 1.05, 1.53) and low-frequency drinking (<7 times/wk) decreased the probability (OR = 0.77; 95% CI: 0.60, 0.94), as did shorter duration of drinking (<20 years) (OR = 0.64; 95% CI: 0.49, 0.79). On subgroup analyses, significant pooled results were observed for studies with high quality, with hospital-based controls and with adjustment for confounding factors, smoking, age, and sex. CONCLUSIONS: The risk of NPC may increase with alcohol consumption. Ever drinking increased the risk versus nondrinking. Additionally, high-frequency drinking increased the risk, but low-frequency drinking decreased it to some extent. Further intensive studies based on well-designed methods are needed to examine the association.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.055 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".