Childhood cancer epidemiology in low‐income countries
Bibliographic record
Abstract
Global studies of childhood cancer provide clues to cancer etiology, facilitate prevention and early diagnosis, identify biologic differences, improve survival rates in low-income countries (LIC) by facilitating quality improvement initiatives, and improve outcomes in high-income countries (HIC) through studies of tumor biology and collaborative clinical trials. Incidence rates of cancer differ between various ethnic groups within a single country and between various countries with similar ethnic compositions. Such differences may be the result of genetic predisposition, early or delayed exposure to infectious diseases, and other environmental factors. The reported incidence of childhood leukemia is lower in LIC than in more prosperous countries. Registration of childhood leukemia requires recognition of symptoms, rapid access to primary and tertiary medical care (a pediatric cancer unit), a correct diagnosis, and a data management infrastructure. In LIC, where these services are lacking, some children with leukemia may die before diagnosis and registration. In this environment, epidemiologic studies would seem to be an unaffordable luxury, but in reality represent a key element for progress. Hospital-based registries are both feasible and essential in LIC, and can be developed using available training programs for data managers and the free online Pediatric Oncology Networked Data Base (www.POND4kids.org), which allows collection, analysis, and sharing of data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".