Sequential Use of Carfilzomib and Pomalidomide in Relapsed Multiple Myeloma: A Report from the Canadian Myeloma Research Group (CMRG) Database
Bibliographic record
Abstract
The treatment of multiple myeloma has dramatically improved due to the availability of novel therapies that are highly effective and are quickly moving into first-line therapy. The Canadian Agency for Drugs and Technologies in Health (CADTH) recently recommended that patients who receive daratumumab should only be eligible to receive either carfilzomib or pomalidomide but not both, for relapsed MM. In order to assess the efficacy of these two agents in the relapsed setting, we utilized our national myeloma database. A total of 121 patients were reviewed, 49 patients received CAR- before POM-based (CAR-POM), and 73 patients received POM- before CAR-based (POM-CAR) therapy. In the groups selected, the median PFS was 4.93 months (95% CI, 2.76-7.07) and 5.36 months (95% CI, 3.75-6.94) for CAR-POM and POM-CAR, respectively. The median OS for patients treated with CAR-POM was 11.01 months (95% CI, 4.50-19.13), and for patients treated with POM-CAR the median OS was 10.98 months (95% CI, 8.98-19.17). In this real-world observational study, we demonstrated that both CAR- and POM-based therapies, irrespective of the order in which they were used, were effective treatment options for patients with advanced relapsed MM.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".