Extrapolated Trend of Cancer Incidences in North Eastern States of India
Bibliographic record
Abstract
The ICMR annual report of 2004-2005 reveals a very high incidence of cancer of all sites in general and tobacco and pesticides related cancer in particular North East region of India.According to the report by Rajesh Dikshit, et.al March 28, 2012 of the center of Global health research, Canada, across states, a 30 year old man in the North East has the highest chance(11.2%) of dying from cancer before 70 years of age.The same report also reveals that this chance of dying is highest(6.0%)for North east women.Due to the magnitude of the disease in the region, it becomes important to make some forecasting study about the intensity of cancer incidences in the future.The present projections based on the extrapolation of trends of age adjusted cancer incidence rate, over time by least squares linear regression model assuming that trends in risk behavior will remain stable and the continuation of the past trend into the future.The states that were selected for the present study are those where data are available for maximum number of years in the past.Based on the reports of Indian Council of Medical Research on population based cancer registries, Mizoram, Manipur, Sikkim and the districts of Kamrup and Dibrugarh in Assam are the regions of North East where the data are available from the year 2002 to 2010.Based on the findings, it becomes clear that the incidences of cancer for all sites can significantly increase in some regions, moderately increase in the others and even decrease in few regions of North East India through the year 2016.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.001 | 0.001 |
| 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".