Early phase study enrollment in Canadian children with cancer near end of life: A retrospective cohort study from Cancer in Young People in Canada
Bibliographic record
Abstract
BACKGROUND: Little is known about children with cancer who participate in early phase (phase 1/2) oncology trials toward the end of life (EOL). The authors sought to describe the proportion of Canadian children with cancer who participate in trials in their last 90 days, explore predictors of enrollment, describe these trials and key EOL outcomes. METHODS: This retrospective cohort study used data from the Cancer in Young People in Canada (CYP-C) database, a national population-based registry. The study included Canadian children and adolescents with cancer 0-20 years old at death, who died from 2001 to 2021. We performed logistic regression to explore the relationships between diagnosis-related factors, treatment-related factors, demographic factors, and study enrollment. RESULTS: Of 3125 children and adolescents who died, 140 (4.5%) met study criteria. Patients with leukemia or lymphoma (odds ratio [OR], 0.54; 95% confidence interval [CI], 0.33-0.87) or a solid tumor (OR, 0.48; 95% CI, 0.29-0.79) enrolled less frequently than those with central nervous system tumors. Previous trial enrollment (OR, 3.02; 95% CI, 2.43-3.77), and initial treatment in a major early phase study center (OR, 1.65; 95% CI, 1.14-2.4) were associated with enrollment. Patients in the lowest census-based income quintiles (quintiles 1-2) enrolled less frequently than those in the highest quintiles (3-5) (OR, 0.56; 95% CI, 0.37-0.84). CONCLUSIONS: A small proportion of children and adolescents participate in early phase trials toward EOL. Diagnosis, treatment-related, and socioeconomic factors are associated with non-enrollment. Clinicians, researchers, and policy developers should consider and address the possible impact of socioeconomic and other factors on access to studies, to ensure equitable access for diverse populations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".