Epidemiologic Pattern of Cancer in Kathmandu Valley, Nepal: Findings of Population-Based Cancer Registry, 2018
Bibliographic record
Abstract
PURPOSE: Although cancer is an important and growing public health issue in Nepal, the country lacked any population-based cancer registry (PBCR) until 2018. In this study, we describe the establishment of the PBCR for the first time in Nepal and use the registry data to understand incidence, mortality, and patterns of cancer in the Kathmandu Valley (consisting of Kathmandu, Lalitpur, and Bhaktapur districts), which comprises 10.5% of the estimated 29 million population of Nepal in 2018. MATERIALS AND METHODS: The PBCR collects information from facilities and communities through the active process. The facilities include cancer or general hospitals, pathology laboratories, hospice, and Ayurvedic centers. In the communities, the field enumerators or female community health volunteers collected the data from the households. In addition, the Social Security and Nursing Division under the Department of Health Services, which provides subsidy for cancer treatment of underprivileged patients, was another major source of data. The collected data were verified for residence, accuracy, and completeness and then entered and analyzed using CanReg5 software. RESULTS: In the Kathmandu Valley, the PBCR registered 2,156 new cancer cases with overall age-adjusted incidence rate for all cancers of 95.7 per 100,000 population (95.3 for males and 98.1 for females). The age-adjusted mortality rate for males was 36.3 (n = 365) and for females 27.0 (n = 305) per 100,000 population. We found that the commonest cancers in males were lung and stomach, whereas in females, they were breast and lung cancer. Gallbladder cancer was among the top five common cancers in both sex. CONCLUSION: These findings provide a milestone to understand the cancer burden in the country for the first time using the PBCR and will be helpful to develop and prioritize cancer control strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".