Who participates in myeloma trials? clinical and geographic predictors of access and outcomes in British Columbia, Canada.
Bibliographic record
Abstract
Abstract Introduction: Clinical trials (CTs) are essential for advancing evidence-based treatment in multiple myeloma (MM), offering patients early access to novel therapies. British Columbia (BC), a geographically large province, more than twice the size of France, has centralized CTs in major urban centers, potentially limiting access for patients in rural areas. The impact of geographic, clinical, and socioeconomic factors (SEFs) on CT participation and outcomes remains poorly understood. This study evaluated predictors of CT participation and its association with overall survival (OS) in a real-world MM cohort in BC. Methods: We conducted a single-center retrospective cohort study of MM patients aged >18 diagnosed between January 2000 and December 2023 who received systemic therapy. CT participation was defined as enrollment in any interventional or observational study providing care beyond standard treatment. Data were collected from a local MM database and chart review. Variables included demographics, SEFs, ECOG performance status, comorbidities, and OS. Univariate and multivariate Cox proportional hazards models were used to identify predictors of CT participation and OS. Results: Among 599 patients, 80 (13.4%) participated in a CT. Compared to non-participants, CT participants lived closer to the center (mean 101 km vs 174 km, p = 0.0002), were more likely to reside in urban areas (90.0% vs 69.6%, p = 0.0002), had better ECOG performance status (p = 0.0006), and fewer comorbidities (mean 1.46 vs 1.83, p = 0.04). No significant differences were observed in income, SES, marital status, ISS stage, or ASCT receipt. On univariate analysis, CT participation was associated with longer OS (HR 0.69; median 121.7 vs 93.9 months). Other significant predictors of OS included younger age (continuous variable, HR 1.05, p< 0.0001), employed status (HR 0.64, p< 0.0001), ECOG status (HR range 1.98-21.14, p< 0.0001), and ASCT (HR 0.40, p< 0.0001). In multivariate analysis, CT participation showed a non-significant trend toward improved OS (HR 0.73, p = 0.07), while age, ECOG status, and ASCT remained independent predictors. Conclusion: In this real-world cohort, CT participants were more likely to reside in urban areas, live closer to the treatment center, and have better performance status and fewer comorbidities. These findings suggest that geographic distance and clinical fitness are key barriers to trial participation, while income and SES were not as influential. Although CT participation was associated with longer OS in univariate analysis, this effect did not reach statistical significance after adjustment, though a favorable trend remained. These results underscore the need to expand trial access to rural and comorbid patients and support prospective studies using matched cohorts to better understand the impact of CT participation on outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".