Childhood Acute Myeloid Leukemia: An Indian Perspective
Bibliographic record
Abstract
Cure rates of childhood malignancies are inferior in India as compared to developed nations. There is paucity of data addressing outcome of childhood acute myeloid leukemia (AML) from India. Hence, this study was designed to assess the outcome of childhood AML in India over the last 2 decades, identify shortcomings and suggest remedial measures. A comprehensive search to identify studies addressing outcome of childhood AML from India was carried out. International Society of Paediatric Oncology annual meeting abstracts were searched to identify unpublished data. Clinicodemographic and outcome data were extracted from these abstracts. Outcomes of <500 patients have been published to date, with predominantly small single-center series from 5 cities. Several AML protocols with modifications to suit the logistics in India have been used. Administration of chemotherapy (standard as well as oral and outpatient based) with manageable toxicity has been deemed feasible. Survival outcomes are modest (23% to 53.8%) except for AML M3 (over 80%), with high early-death rates, relapse, along with abandonment. Few series have identified prognostic parameters and disease burden at diagnosis, and used cytogenetics (for risk stratification) or bone marrow transplant (BMT). There is a need for assessment of risk factors in Indian patients; administration of adequate and appropriate therapy, both upfront and after relapse; improvement in supportive care; and national data management infrastructure with updating/monitoring of registries along with better financial and social support initiatives. These multimodal and additive remedial measures could significantly improve outcome of childhood AML in India by reducing mortality, relapse, and abandonment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".