Clinical evidence for immune-based strategies in early-line multiple myeloma: current challenges in decision-making for subsequent therapy
Bibliographic record
Abstract
Almost all patients with multiple myeloma (MM) will eventually develop disease that has relapsed with or become refractory to available treatments and will require additional therapy. However, data are still lacking on how best to sequence regimens in the relapsed/refractory (RR) setting after the failure of early-line lenalidomide, bortezomib, and/or daratumumab, the most commonly used agents in clinical practice today. With the treatment landscape rapidly changing in response to emerging clinical trial data and approvals of several new drugs and additional combinations, it is critically important to focus on patients with RRMM. Variability in patient baseline characteristics, such as the number of prior lines of treatment, refractoriness to prior treatments, prior stem cell transplant, and timing and dosing of prior lenalidomide, makes it difficult to select the best options for patients with RRMM for whom first-line treatments have failed. The aim of this review is to provide both an overview of current therapies and future directions within the RRMM treatment landscape, and a framework for clinicians to choose the most promising next treatment option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".