Cancer-specific mortality in multiple myeloma: a population-based retrospective cohort study
Bibliographic record
Abstract
Survival has improved in patients diagnosed with multiple myeloma (MM) over the last two decades; however, there remains a paucity of data on the causes of death in MM patients and whether causes of death change during the disease trajectory. We conducted a retrospective population-based study to evaluate the rates of MM-specific versus non-MM cause of death and to identify factors associated with cause-specific death in MM patients, stratified into autologous stem cell transplant (ASCT) and non-ASCT cohorts. A total of 6,677 patients were included, 2,576 in the ASCT group and 4,010 in the non-ASCT group. Eight hundred and seventy-three (34%) ASCT patients and 2,787 (68%) non-ASCT patients died during the follow-up period. MM was the most frequent causes of death, causing 74% of deaths in the ASCT group and 67% in the non-ASCT group. Other cancers were the second leading causes of death, followed by cardiac and infectious diseases. Multivariable analysis demonstrated that a more recent year of diagnosis and novel agent use within 1 year of diagnosis were associated with a decreased risk of MM-specific death, whereas a history of previous non-MM cancer, older age, and the presence of CRAB criteria at diagnosis increased the risk of non-MM death. Our data suggests that despite improvement in MM outcomes in recent years, MM remains the greatest threat to overall survival for patients. Further advances in the development of effective MM therapeutic agents in both ASCT and non-ASCT populations and patient access to them is needed to improve outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".