Supportive medical care for children with acute lymphoblastic leukemia in low- and middle-income countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the last two decades, remarkable progress in the treatment of children with acute lymphoblastic leukemia has been achieved in many low- and middle-income countries (LMIC), but survival rates remain significantly lower than those in high-income countries. Inadequate supportive care and consequent excess mortality from toxicity are important causes of treatment failure for children with acute lymphoblastic leukemia in LMIC. This article summarizes practical supportive care recommendations for healthcare providers practicing in LMIC, starting with core approaches in oncology nursing care, management of tumor lysis syndrome and mediastinal masses, nutritional support, use of blood products for anemia and thrombocytopenia, and palliative care. Prevention and treatment of infectious diseases are described in a parallel paper.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it