Redefining attrition in multiple myeloma (MM): a Canadian Myeloma Research Group (CMRG) analysis
Bibliographic record
Abstract
While most patients diagnosed with multiple myeloma (MM) receive initial therapy, reported attrition rates are high. Understanding attrition rates and characteristics of patients not receiving subsequent therapy is useful for MM stakeholders. We performed an analysis of attrition rates in a large disease-specific database of patients with newly diagnosed MM who received at least one line of therapy between Jan 1/10-Dec 31/20. Attrition was defined as failure to receive a subsequent line of therapy despite progression of MM or due to death. A total of 5548 patients were identified, 3111 autologous stem cell transplant (ASCT) patients and 2437 non-ASCT. In the ASCT cohort, the attrition rate was 7% after line 1, 12% after line 2, and 23% after line 3. In non-ASCT patients, the attrition rate was 19% after line 1, 26% after line 2, and 40% after line 3. Death was the dominant contributor to attrition across all cohorts, with a minority of patients alive with progressive disease in the absence of further therapy at each line. Multivariable analysis identified older age, shorter time to progression, and inferior response as independent risk factors for attrition. Our data show that attrition rates increase with each line of therapy and are higher in non-ASCT patients but are appreciably lower than previously reported. This study supports a revision of the previous definition of attrition, demonstrating that most patients who do not receive subsequent therapy are either continuing their current therapy and/or are in remission off-treatment rather than being irreversibly lost to attrition.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".