Cyclophosphamide-glucocorticoids versus lenalidomide-dexamethasone as treatment for multiple myeloma at first relapse after autologous stem cell transplantation – a retrospective analysis
Bibliographic record
Abstract
There have been significant improvements in therapeutic options for relapsed multiple myeloma (MM) over the past two decades, with many novel agents including proteasome inhibitors, immunomodulatory agents, and more recently monoclonal antibodies demonstrating efficacy in this setting. However, there is a paucity of real-world data comparing outcomes seen in patients treated with novel agents as opposed to older agents. We report a historical single center cohort of patients diagnosed with myeloma between the years 1991-2012 in order to explore possible differences in outcomes. A total of 139 patients who underwent stem cell transplantation were included in our study. In our study, 88 patients were treated with cyclophosphamide and steroids alone at relapse whereas 51 patients were treated with Len-Dex. In the multivariate analysis, TTNT was shorter for patients who received Cyclo compared to Len-Dex (HR=1.74; 95% CI, 1.01-2.99; p=0.04); however, we could not detect an overall survival benefit (HR=1.20; 95% CI 0.63-2.29; p=0.57). Adverse event rates were similar in the two groups. In this retrospective single center analysis, Len-Dex was associated with longer TTNT compared with Cyclo at first relapse following autoSCT in MM; however its effect on overall survival in this setting was less clear.
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 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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".