Disparities in clinical trial enrolment at a Canadian comprehensive cancer centre: A 15-year retrospective study.
Bibliographic record
Abstract
6550 Background: Disparities in clinical trials limit the generalizability of study findings and perpetuate disparities in treatment access and outcomes, but there is a paucity of data in the Canadian context. The objective of this study was to examine disparities in cancer clinical trial enrollment at a large, comprehensive Canadian cancer center. Methods: We conducted a retrospective cohort study of clinical trial enrollment among all newly diagnosed cancer patients (N=154,880) at the Princess Margaret Cancer Centre in Toronto, Canada (2006-2021). Multivariable Bayesian hierarchical logistic regression with random effect for most responsible physician was used to examine the correlates of clinical trial enrollment, comparing the population of enrolled and non-enrolled patients. Odds ratios were adjusted for patient variables (sex, age at diagnosis, language, geography, primary care provider), and census tract-level marginalization (residential instability, material deprivation, dependency, ethnic concentration), disease variables (cancer site and disease stage at diagnosis), and provider variables (most responsible physician (MRP), and MRP’s sex, language, medical training, and department). Results: Overall, 11.2% of patients enrolled (n=17,400) in a clinical trial, with 5-, 10-, and 15-year cumulative incidences of 12%, 15%, and 18%, respectively. Small, but significant differences were observed between enrollees and the overall patient population. 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; p<.001), ≥65 years (AOR vs <40, 0.61; 95% CI, 0.56-0.65; p<.001), non-English language speakers (AOR vs English, 0.72; 95% CI, 0.67-0.77; p<.001), lived ≥250 km away from the cancer center (AOR vs <15km, 0.71; 95% CI, 0.62-0.80; p<.001), or lived in areas with greater material deprivation (AOR, 0.94; 95% CI, 0.93-0.96; p<.001) or higher ethnic concentration (AOR, 0.96; 95% CI, 0.94-0.98; p<.001). Greater odds of enrollment were found in patients with metastatic disease (AOR, 1.19; 95% CI, 1.13-1.25; p<.001) and in those with a primary care provider (AOR, 1.69; 95% CI, 1.55-1.85; p<.001). Conclusions: Disparities were observed in clinical trial enrollment, despite a publicly funded health care system. While broader prospective data collection efforts are critical to better understand the influence of patient, provider and system factors on clinical trial enrollment, these findings suggest the need for targeted strategies to increase diversity in clinical trial access.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".