Survival in multiple myeloma patients who develop second malignancies: a population-based cohort study
Bibliographic record
Abstract
Survival in multiple myeloma (MM) has improved significantly during recent decades both in younger and older patients.1,2 The improved survival is considered to be primarily due to new treatment options in MM, including high-dose melphalan with autologous stem cell transplantation,3 the immunomodulatory drugs and proteasome inhibitors.4,5 Recently, second malignancies have gained great clinical and scientific attention in MM as three randomized clinical trials reported an increase in second malignancies associated with lenalidomide maintenance treatment.6 In a newly published meta-analysis, exposure to lenalidomide plus oral melphalan was found to significantly increase hematologic second malignancies.7 Previously we showed that MM patients had a 26% increased risk of developing any second malignancy when compared to the general population, and an 11-fold increased risk of developing acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS).8 In the United States, second or higher-order malignancies are the third most common cancer diagnoses.9 With improved survival in MM patients, second malignancies are expected to increase in the near future and possibly contribute to problems of disease management. Importantly, it has been shown that the cumulative risk of death from MM outweighs the risk of death due to second malignancies.6 For the individual patient who develops a second malignancy, however, the outcome is of great importance. We conducted a large population-based cohort study, including all patients diagnosed with MM in Sweden, over a period of more than 50 years. This study aimed to investigate the effects of second malignancies on survival and assess changes following the introduction of modern myeloma therapy. Furthermore, as AML/MDS is over-represented in MM patients, we assessed patterns of survival specifically in these patients.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".