Patterns of Cancer Incidence and Mortality in North- Eastern India: The First Report from the Population Based Cancer Registry of Tripura
Bibliographic record
Abstract
BACKGROUND: There is, till date no population-based data regarding cancer patterns in North- Eastern India, dictating the need to understand the epidemiology of cancer in this population for its effective management. METHODS: This is the first report of the Population Based Cancer Registry (PBCR) in Tripura (2010-2014). The protocol involves active collection of data on all cancer cases from Tripura through staff visit in more than 150 sources of incident and mortality registration, government and private hospitals, municipal corporation, etc. and scrutiny, corroboration with existing records. Data was analyzed statistically to understand cancer trends in terms of incidence and mortality across different sites, age groups affected and gender. RESULTS: A total of 10,251 cases were registered during the period, with overall age-adjusted incidence rates of 75.7 and 54.9 per 100,000 males and females respectively. Crude Incidence Rate (CR) and Age- Adjusted Rate (AAR) was among the lowest reported in India, probably due to associated socio-economic factors. The most prevalent cancers were lung (18.1%), esophageal (8.3%) for men and cervix uteri (17.6%), breast (13.8%) for females. Gall bladder cancer in females was one of the highest in the country. Rate of cancer mortality in the population was quite high and significantly increased with time, probably accounting for dearth in early detection and feasible treatment alternatives. CONCLUSION: The data suggests that high cancer incidence and mortality are prevalent in the population of Tripura, dictating the need of active tobacco control measures, early detection and awareness drives for effective cancer control.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".