Global Myeloma Trial Participation and Drug Access in the Era of Novel Therapies
Bibliographic record
Abstract
PURPOSE: The globalization of clinical trials has accelerated recent advances in multiple myeloma (MM). However, it is unclear whether trial enrollment locations are reflective of the global burden of MM and whether access to novel therapies is timely and equitable for countries that participate in those trials. METHODS: To assess this, we characterized where MM trials that led to US Food and Drug Administration (FDA) approvals were conducted and determined how often and quickly these drug regimens received approval in their participating trial countries on the basis of country income level and geographic region. RESULTS: A systematic review was conducted to identify all MM clinical trials that met their primary endpoint, enrolled patients outside the United States, and resulted in FDA approval from 2005 to 2019. A total of 18 pivotal MM clinical trials were identified. High-income countries enrolled patients in 100% (18/18) of the trials identified, whereas upper-middle and lower-middle-income countries were represented in 61% (11/18) and 28% (5/18) of trials, respectively. No patients from low-income countries were enrolled. One trial enrolled patients in sub-Saharan Africa, and no trials enrolled patients in South Asia/Caribbean. For drugs/regimens that were approved in their participating countries, the median time from FDA approval to approval was 10.9 months. There were no drugs approved in lower-middle-income trial countries. MM trials leading to FDA approval are generally run in high-income, European, and Central Asian countries. CONCLUSION: There are substantial disparities in where novel therapies are evaluated and where they are ultimately approved for use on the basis of income level and geography.
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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 | 0.237 | 0.336 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".