Paediatric relapsed acute leukaemia: curative intent chemotherapy improves quality of life
Bibliographic record
Abstract
OBJECTIVES: Paediatric patients with leukaemia with relapse or induction failure have poor prognosis. Anticipated quality of life (QoL) is important in treatment decision making. The objective was to determine if curative intent at relapse or induction failure, when compared with palliative intent, was associated with child's physical health, pain or general fatigue and parents' QoL over time among patients with paediatric leukaemia in El Salvador. METHODS: This was a prospective observational cohort study. Children 2-18 years with acute leukaemia at first relapse or induction failure were eligible. Assessments occurred every 2 months for up to 2 years using validated proxy report and self-report scales, where guardians were the primary respondents. Initial curative or palliative intent was categorised at enrolment by physicians. The impact of initial intent on QoL was assessed using linear mixed effects models and interaction between QoL and time. RESULTS: Of the 60 families enrolled, initial treatment intent was curative in 31 (51.7%) and palliative in 29 (48.3%). During the 2-year observation period, 44 children died. Initial curative intent significantly improved child's physical health (estimate=8.4, 95% CI 5.1 to 11.6), pain (estimate=5.4, 95% CI 1.5 to 9.2) and fatigue (estimate=6.6, 95% CI 3.2 to 9.9) compared with palliative intent, but not parents' QoL (estimate=1.0, 95% CI -0.8 to 2.8). CONCLUSIONS: Among paediatric patients with acute leukaemia at relapse or induction failure, initial curative intent treatment plan was associated with better physical health, pain and fatigue when compared with palliative intent. A curative approach may be a reasonable option for patients with acute leukaemia even when prognosis is poor.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".