Differences in cancer incidence and pattern between urban and rural Nepal: one-year experience from two population-based cancer registries
Bibliographic record
Abstract
Variations in cancer incidence, mortality and pattern exist in rural and urban areas. Understanding these differences helps in developing targeted cancer prevention and control strategies. However, no previous studies have explored the differences in cancer demographics between the rural and urban areas of Nepal. The data of Kathmandu Valley (urban area) Population-Based Cancer Registry (PBCR) and Rukum (rural area) PBCR were analysed to identify the differences in cancer pattern in rural and urban areas. The age-adjusted incidence rate (AAR) in Kathmandu was higher than that in Rukum (1.6 times among males and 1.9 times among females). The top two leading sites in males were lungs and stomach in both the regions; however, the rates were higher in Kathmandu. The incidence rate for cancer of the urinary bladder among males in Kathmandu was particularly higher - 4.4 times that of Rukum. In females, the leading site of cancer in Kathmandu was breast, which was eight times higher compared to Rukum, whereas the incidence rate of cervix cancer in Kathmandu is 30% less than in Rukum. The incidence of tobacco-related cancer was found to be higher in Kathmandu compared to Rukum. These findings reveal the need for different policy priorities for cancer control in the urban versus rural regions of Nepal, based on the different demographics of cancer in the two areas. Similar studies from other regions of Nepal are needed to develop a targeted cancer control strategy.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".