Early M‐Protein Dynamics Predicts Progression‐Free Survival in Patients With Relapsed/Refractory Multiple Myeloma
Bibliographic record
Abstract
This study aimed to predict long-term progression-free survival (PFS) using early M-protein dynamic measurements in patients with relapsed/refractory multiple myeloma (MM). The PFS was modeled based on dynamic M-protein data from two phase III studies, POLLUX and CASTOR, which included 569 and 498 patients with relapsed/refractory MM, respectively. Both studies compared active controls (lenalidomide and dexamethasone, and bortezomib and dexamethasone, respectively) alone vs. in combination with daratumumab. Three M-protein dynamic features from the longitudinal M-protein data were evaluated up to different time cutoffs (1, 2, 3, and 6 months). The abilities of early M-protein dynamic measurements to predict the PFS were evaluated using Cox proportional hazards survival models. Both univariate and multivariable analyses suggest that maximum reduction of M-protein (i.e., depth of response) was the most predictive of PFS. Despite the statistical significance, the baseline covariates provided very limited predictive value regarding the treatment effect of daratumumab. However, M-protein dynamic features obtained within the first 2 months reasonably predicted PFS and the associated treatment effect of daratumumab. Specifically, the areas under the time-varying receiver operating characteristic curves for the model with the first 2 months of M-protein dynamic data were ~ 0.8 and 0.85 for POLLUX and CASTOR, respectively. Early M-protein data within the first 2 months can provide a prospective and reasonable prediction of future long-term clinical benefit for patients with MM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".