Patient characteristics, treatment patterns, and outcomes among black and white patients with multiple myeloma initiating daratumumab: A real-world chart review study
Bibliographic record
Abstract
BACKGROUND: Daratumumab was approved for multiple myeloma (MM) in 2015. While its safety and efficacy are well documented, there is limited real-world information on its use and outcomes in patients of different races. METHODS: We conducted a retrospective chart review of adult patients with MM initiating daratumumab in any line of therapy (LOT) between November 2015 and May 2020. De-identified data were retrieved from 2 US clinical sites; patient characteristics, treatment patterns, and response rate were described for black and white patients, stratified by LOT. Overall response rate (ORR), progression-free survival (PFS), and time to next LOT (TTNT) were compared between black and white patients initiating daratumumab in second line (2L) or later, adjusting for age and number of prior lines. RESULTS: Two hundred and fifty-two patient charts (89 black, 163 white) were extracted. Black patients were younger at diagnosis (61.7 vs. 67.0 years) and had a similar proportion of females (black: 44.9%, white: 46.6%). Black patients had longer time between MM diagnosis and daratumumab initiation (43.2 vs. 34.1 months) and received more prior LOTs (median 3.0 vs. 2.0). ORR for black and white patients initiating daratumumab in 1L was 100.0%, with very good partial response or better in 75.0% and 66.7%, respectively. Similar trends were observed in 2L and 3L+. There were no significant differences in ORR, PFS, or TTNT between groups. CONCLUSION: Daratumumab had similar clinical outcomes (ORR, PFS, and TTNT) in black and white patients. Black patients initiated daratumumab later in their treatment, suggesting potential discrepancies in access to new MM treatments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".