Incidence and predictors of treatment-related mortality in paediatric acute leukaemia in El Salvador
Bibliographic record
Abstract
Survival rates among children with leukaemia in low-income countries are lower than those in high-income countries. This has been attributed in part to higher treatment-related mortality (TRM). We examined the demographics, treatment, and outcomes of paediatric patients in El Salvador with acute lymphoblastic leukaemia (ALL) or acute myeloid leukaemia (AML) to determine the incidence, causes, and risk factors for TRM. Two trained data managers collected data prospectively; no patients were excluded. Biological, socioeconomic and nutritional predictors were examined. A total of 469 patients with ALL and 78 patients with AML were included. The 2-year cumulative incidence of TRM was significantly higher among children with AML (35.4+/-6.4%) than those with ALL (12.5+/-1.7%; P<0.0001). However, the proportion of deaths attributable to the toxicity of treatment did not differ significantly between AML (25/47, 53.2%) and ALL (55/107, 51.4%; P=0.98). Among children with ALL, low monthly income (P=0.04) and low parental education (P=0.02) significantly increased the risk of TRM. Among children with AML, biological, socioeconomic, and nutritional variables were not associated with TRM. In this low-income country, toxic death significantly contributes to mortality in both ALL and AML. A better understanding of the effect of socioeconomic status on TRM may suggest specific strategies for patients with ALL.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".