Treatment strategies and regimens of graduated intensity for childhood acute lymphoblastic leukemia in low‐income countries: A proposal
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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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".