Pediatric ALL Treatment Modifications in Low- and Middle-Income Countries: A Systematic Review
Bibliographic record
Abstract
PURPOSE For children with ALL in low- and middle-income countries (LMICs), treatment regimen adaptation based on local contexts is often necessary. However, the clinical impact of such modifications is poorly understood. The purpose of this study is to examine pediatric ALL treatment regimens used in LMICs, assess for patterns in adaptation to identify common barriers affecting global delivery of ALL care, and describe the breadth of outcomes. METHODS Using the PRISMA guidelines, a systematic review was conducted, across seven databases, of ALL regimens use in LMICs in 2000-2021, documenting the geographic distribution of treatment backbone adoption, regimen modifications, and outcomes. Article characteristics were summarized using descriptive statistics. RESULTS Of 13,900 articles, 125 met abstraction criteria. Data spanned 36 countries (6.4% low-income, 43.2% lower-middle–income, 50.4% upper-middle–income) and 163 regimens, of which 138 (84.6%) referenced a high-income ALL collaborative group regimen as a backbone. Sixty-four percent of regimens (n = 104) were adapted. Individual modifications (n = 390) were consolidated into 73 distinct regimen changes; reduction/omission of high-dose methotrexate, observed in 30 modified regimens (28.8%), was the most common. Implementation challenges, such as drug access and cost, were cited more frequently than toxicity as the rationale for modification; however, implementation outcomes (eg, feasibility, cost) were only measured in 6.4% of articles. Across all outcomes, 5-year overall survival was higher with modified versus unmodified regimens ( P = .030). CONCLUSION Although implementation barriers are primary drivers of ALL regimen adaptations globally, the paucity of reported implementation outcomes represents a methodological gap in the literature. Incorporating implementation science methods and frameworks is critical for the timely and effective delivery of innovative treatment regimens across resource settings.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".