Outcomes of treatment for relapsed acute lymphoblastic leukaemia in children with <scp>D</scp>own syndrome
Bibliographic record
Abstract
Children with Down syndrome (DS) have a greater risk for developing both acute lymphoblastic leukaemia (ALL) and significant adverse effects of chemotherapy. We investigated their outcome with, and tolerance of, treatment protocols for relapsed ALL optimized in the paediatric population without DS. Probability of survival and causes of treatment failure were determined for 49 children with DS and a matched cohort of 98 children without DS among 2160 children treated for relapsed ALL in clinical trials conducted by the Berlin-Frankfurt-Münster ALL Relapse Study Group between 1983 and 2012. Despite more favourable ALL relapse characteristics, children with DS experienced lower event-free (EFS) and overall survival (OS) than the control group without DS (EFS 17 ± 08% vs. non-DS 41 ± 06%, P = 0·006; OS 17 ± 09% vs. non-DS 51 ± 06%, P < 0·001). Children with DS developed more frequently fatal complications of treatment (34 ± 07% vs. non-DS 10 ± 04%, P < 0·001). During the last decade, EFS and OS were no longer significantly different in children with and without DS (EFS 31 ± 09% vs. 36 ± 09%, P = 0·399; OS 31 ± 12% vs. 53 ± 09%, P = 0·151). DS proved an independent prognostic factor of outcome after ALL relapse. Induction deaths and treatment-related mortality but not subsequent relapse were the main barrier to successful outcomes of relapse therapy in children with DS.
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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 | 0.001 |
| 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 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".