Childhood cancer in Uruguay: 1992–1994. Incidence and mortality
Bibliographic record
Abstract
BACKGROUND: The referral of all children with cancer in Uruguay to a single center affords the opportunity to generate population-based incidence and mortality rates in this developing country in Latin America. PROCEDURE: All incident cases of cancer in children, 0-14 years of age, were ascertained from a combination of three sources for the period January 1992-December 1994. Diagnoses were grouped according to the International Classification of Childhood Cancer. Information on the size and age distribution of the total population was obtained from national census records. Follow-up was undertaken until December 1999 to afford a minimum interval of 5 years and the determination of mortality rates. RESULTS: The average annual incidence was 133.6 cases of cancer per million children per year and the disease distribution was similar to that in industrialized countries, with the exception of a higher rate and younger age distribution for the Hodgkin disease. The overall age-standardized mortality rate from cancer in childhood, at 6.5 per 100,000, was approximately twice that in the United States and Canada. CONCLUSIONS: Basic indicators of development suggest that Uruguay is more akin to the countries of North America and Western Europe than to those in the developing world. An opportunity has been identified to improve the outcome for children with cancer in this country.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".