Disparities in clinical trial enrollment at a Canadian comprehensive cancer center: A 15‐year retrospective study
Bibliographic record
Abstract
INTRODUCTION: Disparities in clinical trials (CTs) enrollment perpetuate inequities in treatment access and outcomes, but there is a paucity of Canadian data. The objective of this study was to examine disparities in cancer CT enrollment at a large Canadian comprehensive cancer center. METHODS: Retrospective study of CT enrollment among new patient consultations from 2006 to 2019, with follow-up to 2021 (N = 154,880), with the primary outcome of enrollment as a binary variable. Factors associated with CT enrollment were evaluated using multivariable Bayesian hierarchical logistic regression with random effects for most responsible physician (MRP) and geography, adjusted for patient characteristics (sex, age, language, geography, and primary care provider [PCP]), area-level marginalization (residential instability, material deprivation, dependency, and ethnic concentration), disease (cancer site and stage), and MRP (department, sex, language, and training). A sensitivity analysis of the cumulative incidence of enrollment was conducted to account for differences in disease type and follow-up length. RESULTS: CT enrollment was 11.2% overall, with a 15-year cumulative incidence of 18%. Lower odds of enrollment were observed in patients who were female (adjusted odds ratio [AOR], 0.82; 95% confidence interval [CI], 0.78-0.86), ≥65 years (AOR vs. <40, 0.61; 95% CI, 0.56-0.66), non-English speakers (0.72; 95% CI, 0.67-0.77), living ≥250 km away (AOR vs. <15 km, 0.71; 95% CI, 0.62-0.80), and without a PCP. Disease characteristics accounted for the largest proportion of observed variation (20.8%), with significantly greater odds of enrollment in patients with genitourinary cancers and late-stage disease. CONCLUSION: Significant sociodemographic disparities were observed, suggesting the need for targeted strategies to increase diversity in access to cancer CTs in Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".