Global Access to Multiple Myeloma Therapies
Bibliographic record
Abstract
PURPOSE: Initial reports indicate that access to contemporary therapies currently used in North America for the treatment of multiple myeloma (MM) varies internationally. No studies have quantitatively reported the extent of disparities in the access to MM therapies worldwide, with a goal to investigate access to MM therapies and barriers globally. METHODS: From June 18 to July 15, 2023, an electronic survey was distributed to 176 oncologists treating MM outside the United States. MM drugs were categorized by accessibility, with the cutoff for adequate access set at 60% of respondents affirming easy/moderate access. RESULTS: Ninety-five (54%) respondents from 33 countries completed the survey. Fifty-one percent of the respondents were from university-based academic programs, and 17% of the responders treated only plasma cell disorders. Most respondents had adequate access to noncellular MM therapies, except for isatuximab, ixazomib, selinexor, and elotuzumab. Among the cellular therapies, 17% had access to Chimeric Antigen Receptor T-cell therapy, whereas 23% had access to approved T-cell engagers (TCEs). Financial stress on patients and health care systems has emerged as a primary barrier to global inaccessibility of treatment drugs. CONCLUSION: Global access to novel MM therapies remains challenging, and we have identified barriers and suggested strategies to bridge this gap.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".