Treatment of patients with multiple myeloma progressing on frontline-therapy with lenalidomide
Bibliographic record
Abstract
Over the last years, there has been great progress in the treatment of multiple myeloma with many new agents and combinations having been approved and being now routinely incorporated into treatment strategies. As a result, patients are experiencing benefits in terms of survival and better tolerance. However, the multitude of treatment options also presents a challenge to select the best options tailored to the specific patient situation. Lenalidomide is increasingly being used as part of frontline therapy in newly diagnosed multiple myeloma. This agent is typically administered until disease progression. It is currently unclear, how to best manage patients, who relapse while receiving lenalidomide as part of their frontline treatment. We conducted a review to summarize the available evidence in this setting. Our summary shows that there are very few data from current trials testing new combinations based on carfilzomib, pomalidomide, or daratumumab that address this specific patient population. Our review is aimed to summarize the available evidence to assist treatment decision making and to raise awareness of this lack of data to encourage further analyses and the incorporation of sequencing questions in future trial designs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".