Burden of Tobacco-related Cancers in India and its States, 2015-2025
Bibliographic record
Abstract
Tobacco use is a significant contributor to the cancer burden, which is preventable. It relates to around a quarter to two-third of cancers in males and up to half in females. For planning and effective implementation of anti-tobacco programmes, the knowledge of burden of tobacco-related cancers (TRCs) is essential. This paper assesses and projects the burden of TRCs in India and its States for the period 2015-2025 by using the data of National Cancer Registry Programme (NCRP) of Indian Council of Medical Research (ICMR). Cancer incidence rates generated by Population-Based Cancer Registries under the NCRP and population of India and its States as projected by the Registrar General of India formed the sources of data for this study. The best possible assessment of incidence rates was made for the States and Union Territories using the limited data available. The regression method was applied to assess the trend and project the rates for the study period. Overall burden of TRCs in India was estimated at about 365 thousand in 2015 and projected to increase to 506 thousand by 2025, an increase of more than one-third. A sizeable portion of this burden is due to tobacco use in men. Analysis showed regional diversity in the burden of diverse types of TRCs. There is an urgent need to initiate focused tobacco prevention measures to combat this threat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".