Systematic review with meta‐analysis: race‐specific effects of alcohol and tobacco on the risk of oesophageal squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Oesophageal squamous cell carcinoma (OSCC) is associated with alcohol use, tobacco use and African or Asian descent. However, little is known about how racial background modifies the effects of alcohol or tobacco. AIM: To investigate how racial and geographical background modifies the effect of alcohol and tobacco on OSCC via a systematic review and meta-analysis of published literature. METHODS: We performed a literature search in multiple online databases regardless of language. Eligible studies were population-based assessments of the effect of tobacco and/or alcohol on the risk of OSCC allowing stratification by race. The quality of studies was assessed by the Newcastle-Ottawa Scale. Meta-analyses were performed to estimate summary effects using random effect models. RESULTS: Systematic review identified 9668 unique citations of which 34 were eligible. The majority were of high quality. The effect of current smoking vs. never-smoking was weaker among Asians than among Europeans [European: odds ratio (OR) = 4.21, 95% confidence interval (CI) 3.13, 5.66; Asian: OR = 2.31, 95% CI 1.78, 2.99], with the 95% CIs not crossing, indicating statistical significance. Asians also trended towards weaker effects of long-duration cigarette use and of heavy daily cigarette use. There was no difference in the effect of alcohol on OSCC risk by race. CONCLUSIONS: Contrary to our hypothesis, a weaker effect of tobacco for OSCC was observed among Asians than among Europeans. Differences in other factors must explain the higher incidence of OSCC among Asians. More studies are needed to understand the cause of the disparate incidence of OSCC between races.
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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 | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".