Predictors of clinical trial enrollment and impact on outcome in children and adolescents with acute lymphoblastic leukemia: A population based study.
Bibliographic record
Abstract
7031 Background: Outcomes in pediatric acute lymphoblastic leukemia (ALL) have shown remarkable improvements in large part due to sequential clinical trials. Concerns however persist around whether access to clinical trials is equitable. It is also unclear whether patient outcomes are improved simply by enrolling on a clinical trial. Our objective was to therefore determine which patient and disease-related factors are associated with enrollment, and whether enrollment was associated with clinical outcomes among children and adolescents with ALL in a single-payer health system in Ontario, Canada. Methods: We included all Ontario patients diagnosed with ALL between 0-18 years of age from 2002-2012 treated at a pediatric center, identified through a provincial pediatric cancer registry. Clinical trial availability was determined by whether each patient’s primary institution had an open frontline trial for which the patient was eligible at the time of their diagnosis, considering individual disease characteristics such as lineage, central nervous system (CNS) status and risk group. Demographic, disease, trial enrolment, and outcome data were obtained through chart abstraction. Logistic regression models determined factors associated with trial enrolment, while Cox proportional hazard models determined factors associated with event-free and overall survival (EFS, OS). Results: Of 858 patients, 693 (81%) were eligible for an open clinical trial at their time of diagnosis. 476 (69%) enrolled on a trial. In adjusted analyses, age > 15 years (odds ratio 0.4 vs. age 5-9, 95th confidence interval (95CI) 0.2-0.8; p = 0.01) and CNS3 disease (OR 0.38 vs. CNS1, 95CI 0.17-0.83; p = 0.01) were significantly associated with decreased likelihood of enrolment, while sex and neighborhood income quintile were not associated with enrolment. Adjusted for disease and demographic factors, clinical trial enrolment was not significantly associated with either EFS (hazard ratio (HR) 1.1, 95CI 0.7-1.7; p = 0.83) or OS (HR 1.3, 95CI 0.7-2.5; p = 0.44). Conclusions: The majority of patients with ALL eligible for available clinical trials at their time of diagnosis were enrolled. While no disparities in enrolment by income status were noted, adolescents were substantially less likely to participate in trials even within pediatric centers. Studies of mechanisms underlying this disparity are warranted in order to design and implement effective interventions targeting increased enrolment rates in this patient population. Our results however also suggest that clinical trial enrolment on its own is not associated with improved outcomes in the context of a single payer health system.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".