Data from Uncovering Risks Associated with Smoking Types and Intensities in Esophageal Cancer within High-Prevalence Regions in Africa: A Comprehensive Meta-analysis
Bibliographic record
Abstract
Abstract Tobacco is usually cited among the well-known risk factors of esophageal cancer; nevertheless, the extent of the contribution of the type of smoking and its intensity to the disease has not been comprehensively elucidated in Africa. We searched MEDLINE/PubMed, Excerpta Medica Database, Web of Science, Scopus, Cochrane Library, and African Journals Online studies published before September 2023. The quality of the studies was assessed using the Newcastle–Ottawa scale, and the funnel plot was used for assessing potential publication bias. Meta-analyses were conducted to estimate summary effects using random-effects models. This study included 22,319 participants from 27 studies. The results strongly indicate a significant association between tobacco use and a higher risk of esophageal cancer. The risk of esophageal cancer is notably higher among pipe smokers [OR = 4.68; 95% confidence interval (CI), 3.38–6.48], followed by hand-rolled cigarette smokers (OR = 3.79; 95% CI, 2.68–5.35), in comparison with those who smoked commercially manufactured cigarettes (OR = 2.46; 95% CI, 1.69–3.60). Our findings also showed that the risk of esophageal cancer is highest in people smoking >183 packs per year (OR = 5.47; 95% CI, 3.93–7.62), followed by those smoking 93 to 183 packs per year (OR = 3.90; 95% CI, 3.13–4.86), in comparison with those smoking ≤92 packs per year (OR = 2.90; 95% CI, 2.19–3.84). Our findings strongly show that among the different types of tobacco use in Africa, pipe and hand-roller smokers face a higher risk of esophageal cancer.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.060 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".