Comparative efficacy of ciltacabtagene autoleucel versus idecabtagene vicleucel in the treatment of patients with relapsed or refractory multiple myeloma previously treated with 2–4 prior lines of therapy: a matching-adjusted indirect comparison
Bibliographic record
Abstract
Objective To estimate the comparative efficacy of ciltacabtagene autoleucel (cilta-cel) versus idecabtagene vicleucel (ide-cel) in patients with relapsed/refractory multiple myeloma (RRMM) treated with 2–4 prior lines of therapy.Methods Matching adjusted indirect comparison (MAICs) were performed using individual patient-level data (IPD) for cilta-cel from CARTITUDE-1 and CARTITUDE-4 and published aggregated data for ide-cel from KarMMa-3. Cilta-cel patients who met inclusion criteria from KarMMa-3 were selected, and outcomes were compared against data for ide-cel using simulated IPD derived from aggregate-level data from KarMMa-3. Patient characteristics were adjusted by reweighting cilta-cel IPD to match the distribution of prognostic factors in KarMMa-3. Comparative efficacy was estimated for response outcomes using a weighted logistic regression analysis and for progression-free survival using a weighted Cox proportional hazards model.Results Patients treated with cilta-cel were 1.2 times more likely to achieve overall response (relative response ratio [RR]: 1.18 [95% confidence interval: 1.03–1.34]; p = 0.04), 1.3 times more likely to achieve very good partial response or better (RR: 1.34 [1.15–1.57]; p = 0.003), and 1.9 times more likely to achieve complete response or better (RR: 1.91 [1.54–2.37]; p < 0.0001) versus ide-cel patients from KarMMa-3. Cilta-cel was associated with a significant 49% reduction in risk of disease progression or death versus ide-cel (hazard ratio: 0.51 [95% confidence interval: 0.31, 0.84]; p = 0.0078).Conclusion For patients with triple-class exposed RRMM treated with 2–4 prior lines of treatment, cilta-cel was found to provide superior clinical benefit over ide-cel in terms of response and progression-free survival.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".