Treatment of patients with multiple myeloma progressing on frontline-therapy with lenalidomide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 | 0.000 |
| 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 it