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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".