Late Adverse Effects after Treatment for Childhood Acute Leukemia
Bibliographic record
Abstract
The aim of this review is to raise awareness and knowledge among healthcare professionals and policymakers about late adverse effects in survivors of childhood leukemia. With contemporary treatment, over 90% of children with acute lymphoblastic leukemia (ALL) and over 60% with acute myeloid leukemia (AML) are cured. Large cohort studies demonstrate that 20% of ALL and most AML survivors have at least one chronic health condition by 20-25 years after diagnosis. These are life-changing or threatening in some survivors and contribute to increased premature mortality. We describe the frequency, causes, clinical features, and natural history of the most frequent and severe late adverse effects in childhood leukemia survivors, including subsequent malignant neoplasms, metabolic toxicity, gonadotoxicity and impaired fertility, endocrinopathy and growth disturbances, bone toxicity, central and peripheral neurotoxicity, cardiotoxicity, psychosocial late effects, accelerated ageing and late mortality. The wide range of late effects in survivors of haemopoietic stem cell transplant is highlighted. Recent developments informing the approach to long-term survivorship care are discussed, including electronic personalized patient-specific treatment summaries and care plans such as the Survivor Passport (SurPass), surveillance guidelines and models of care. The importance of ongoing vigilance is stressed given the increasing use of novel targeted drugs with limited experience of long-term outcomes. CONCLUSION: It is vital to raise awareness of the existence and severity of late effects of childhood leukemia therapy among parents, patients, health professionals, and policymakers. Structured long-term surveillance recommendations are necessary to standardize follow-up care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".