Treatment strategies and regimens of graduated intensity for childhood acute lymphoblastic leukemia in low‐income countries: A proposal
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
Cure rates for children with acute lymphoblastic leukemia (ALL) are 80-85% in high-income countries (HICs) in North America and Western Europe. However, cure rates are much lower in many low-income countries (LICs), where most cases of ALL occur. Over the past several decades partnerships ("twinning") between HIC and LIC pediatric oncology programs have led to major improvements in outcome for children with ALL in some LICs, often by developing time and resource intensive relationships that allow LIC centers to treat children with regimens similar or identical to those used in HICs. However, the resources are not available in most LICs to allow immediate introduction of intensive ALL treatment regimens similar to those used in HICs. With these thoughts in mind, we present a proposal for a systematic and graduated approach to ALL diagnosis, risk classification, and treatment in LICs. We have based the strategy and the proposed regimens on those developed by the Children's Cancer Group (CCG) and Children's Oncology Group (COG) over the past several decades, beginning with a first level regimen similar to CCG therapy of the early 1980s and then layering on successive treatment intensifications proven effective in randomized clinical trials. Simple monitoring rules are included to help centers decide when they are ready to add new treatment components. This proposal provides a framework that LIC centers can use to provide effective ALL therapy, particularly in regions of the world where few children are currently being cured.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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