Lack of clarity in the definition of treatment-related mortality: pediatric acute leukemia and adult acute promyelocytic leukemia as examples
Bibliographic record
Abstract
Treatment-related mortality (TRM) is important in acute lymphoblastic leukemia and acute myeloid leukemia (AML); however, little is known about how TRM is defined across trials. Two major problems are related to what constitutes treatment versus disease-related cause of death and to TRM attribution (for example, death because of infection or hemorrhage). To address the former, we conducted a systematic review of randomized therapeutic pediatric acute leukemia and adult/pediatric acute promyelocytic leukemia trials and any study type focused on TRM in pediatric acute leukemia. We described definitions used for TRM. Sixty-six studies were included. Few therapeutic pediatric acute lymphoblastic leukemia studies (2/32, 6.3%) provided definitions for TRM, whereas more therapeutic pediatric AML studies (6/9, 66.7%) provided definitions. There was great heterogeneity in TRM classification. The authors of most studies relied on deaths during induction or in remission to delineate whether a death was TRM. However, 44.4% of therapeutic AML studies used death within a specific time frame to delineate TRM. We suggest that a consistent approach to defining and determining attribution for TRM in acute leukemia is an important future goal. Harmonization of definitions across the age spectrum would allow comparisons between pediatric and adult studies.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".