Improving outcomes for children with cancer in low‐income countries in Latin America: A report on the recent meetings of the Monza International School of Pediatric Hematology/Oncology (MISPHO)‐Part I
Bibliographic record
Abstract
The difference in survival for children diagnosed with cancer between high- and low-income countries (LIC) continues to widen as curative therapies are developed in the former but not implemented in the latter. In 1996, the Monza International School of Pediatric Hematology/Oncology (MISPHO) was founded in an attempt to narrow this survival gap. During its sixth and seventh meetings, members recognized the problem of lack of affordability of essential drugs to treat childhood cancer in many LIC, and initiated an advocacy program. In 1998, MISPHO spawned a collaboration of Central American pediatric oncology centers: the Asociación de Hemato-Oncología Pediátrica Centroamericana (AHOPCA). AHOPCA members reported preliminary findings from several of the 10 cooperative protocols that are currently in progress. In 2003, a second regional collaborative group was formed that includes seven centers in South America. Twinning programs between MISPHO centers and centers in high-income countries (HIC) have proven invaluable to harness the resources of these centers to improve pediatric oncology care in LIC. MISPHO educational efforts include oncology nursing, supportive care, cancer-specific updates, epidemiology, and clinical research methods. Educational efforts are facilitated by educational content and online conferencing via www.cure4kids.org. Identifying preventable causes of abandonment of therapy and documenting the nutritional status of patients treated at MISPHO centers are areas of active research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".