Protocol‐based treatment 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 II
Bibliographic record
Abstract
Pediatric cancer programs in low-income countries (LIC) can improve outcomes. However, treatment must be tailored to the patient's living conditions and the availability of supportive care. In some cases, a more intense regimen will decrease survival since the increase in death from toxicity may exceed any decrease in relapse. Attempts to practice evidence-based pediatric oncology are thwarted by the lack of evidence derived from local experience in LIC to determine optimal therapy. This report summarizes treatment regimens used by pediatric oncologists from 15 countries of the Caribbean, Central and South America who participate in the Monza International School of Pediatric Hematology/Oncology (MISPHO). Patients with hepatoblastoma, Wilms tumor, and histiocytosis treated on unmodified published protocols had outcomes comparable to those in high-income countries (HIC). Those with rhabdomyosarcoma, osteosarcoma, Hodgkin lymphoma, and acute myeloid leukemia treated with unmodified regimens had event-free survival estimates 10%-20% lower than those reported in HIC due to higher rates of toxic death, abandonment of therapy, and relapse. Treatment of retinoblastoma is complicated by advanced stages and extraocular disease at diagnosis; improved outcomes depend on education of pediatricians and the public to recognize early signs of this disease. Use of unmodified protocols for Burkitt lymphoma and acute lymphoblastic leukemia have been associated with unacceptable toxicity in LIC, so MISPHO centers have modified published regimens by giving lower doses of methotrexate and reducing use of anthracyclines. Despite the use of all-trans-retinoic acid during induction for acute promyelocytic leukemia, the incidence of fatal hemorrhage remains unacceptably high.
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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 | 0.010 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".