Association of Tobacco Use and Cancer Incidence in India: A Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To estimate the strength of the association between tobacco use and cancer incidence among the Indian population. MATERIALS AND METHODS: Data from PubMed, Embase, and Virtual Health Library were searched from inception of databases till April 30, 2022. There were no restrictions except for English language and human study. Case-control and cohort studies on cancer incidence in relation to tobacco use were selected. Data were extracted independently by two investigators, and discrepancies were resolved by a third reviewer. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The quality assessment was done using the Newcastle Ottawa Scale. RESULTS: The majority were case-control designs (60, 89.6%), covering diverse geographic regions, with Maharashtra (18, 30%) and Kerala (12, 20%) being the most studied. Pooled effect sizes were calculated using the random-effects model, and forest plots were generated. The risk of any cancer associated with smoked and smokeless tobacco was 2.71 (95% CI, 2.25 to 3.16) and 2.68 (95% CI, 2.22 to 3.14), respectively, indicating similar risks. Gender-wise, smoked tobacco had an association of 2.35 (95% CI, 2.05 to 2.65) for males, whereas for smokeless tobacco, it was 1.77 (95% CI, 1.47 to 2.07) for males and 2.34 (95% CI, 1.26 to 3.42) for females. Regardless of gender, tobacco type, and affected body parts, the risk of cancer due to tobacco use was consistent in the Indian population. Site-specific analysis showed higher risks of respiratory system cancers of 4.97 (95% CI, 3.62 to 6.32) and head and neck cancers of 3.95 (95% CI, 3.48 to 4.42). CONCLUSION: This study underscores that both smoked and smokeless tobacco are equally harmful to human health among the Indian population, providing insights for stakeholders and policymakers to arrive at tobacco-specific interventions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it