Enrollment on clinical trials does not improve survival for children with acute myeloid leukemia: A population‐based study
Bibliographic record
Abstract
BACKGROUND: It is questionable whether enrollment on clinical trials offers any survival advantage at the population level over standard-of-care treatment. The objectives of this study were to describe the impact of trial enrollment on event-free survival and overall survival in pediatric acute myeloid leukemia (AML) using the Cancer in Young People in Canada (CYP-C) database. METHODS: Children were included if they had had AML newly diagnosed between ages birth and 14 years from 2001 to 2012. CYP-C is a national pediatric cancer population-based database that includes all cases of pediatric cancer diagnosed and treated at 1 of the 17 tertiary pediatric oncology centers in Canada. Univariate and Cox proportional hazards models were used to evaluate the impact of initial trial enrollment on survival. RESULTS: In total, 397 eligible children with AML were included in the analysis, of whom 94 (23.7%) were enrolled on a clinical trial at initial diagnosis. The most common reason for non-enrollment was that no trial was available. The event-free survival rate at 5 years was 57.8% ± 5.2% for those enrolled versus 54.8% ± 2.9% for those not enrolled (P = .75). The overall survival rate at 5 years was 70.1% ± 4.9% for those enrolled versus 66.3% ± 2.8% for those not enrolled (P = .58). Enrollment on a trial was not associated with improved event-free or overall survival in multiple regression analyses. CONCLUSIONS: Enrollment on a clinical trial was not associated with improved survival for children with AML in a population-based cohort. Rationale for trial enrollment should not include the likelihood of benefit compared with non-enrollment.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".