Use of Bisphosphonates in Elderly Patients With Newly Diagnosed Multiple Myeloma
Bibliographic record
Abstract
Background: Bisphosphonates reduce skeletal-related events (SREs) in patients with multiple myeloma (MM) and, in some studies, improved survival. Since 2011, bisphosphonate use has been recommended by NCCN for all patients with newly diagnosed MM receiving antineoplastic therapy independent of the presence of bone disease. This study investigated their use after these guidelines were established. Methods: We identified patients aged ≥65 years in the SEER-Medicare database with newly diagnosed MM between January 1, 2012, and December 31, 2013, who received antineoplastic therapy, had ≥6 months of follow-up, and did not receive prior bisphosphonates. Presence of SREs at diagnosis was identified, including pathologic fracture, spinal cord compression, radiation to bone, or surgery to bone. Use of bisphosphonates was defined as having ≥1 claim for an intravenous or oral bisphosphonate within 6 months after the start of antineoplastic therapy. We used multivariable modeling to compare users with nonusers, controlling for demographic and clinical covariates. We compared overall survival between users and nonusers using proportional hazards analysis. Results: Of 1,309 patients identified, 720 (55%) used a bisphosphonate. Factors associated with use included SRE at diagnosis (adjusted odds ratio [AOR], 2.60; 95% CI, 1.98–3.40), hypercalcemia (AOR, 1.74; 95% CI, 1.26–2.41), and use of proteasome inhibitor + immunomodulatory imide therapy (AOR, 1.70; 95% CI, 1.21–2.39). Chronic kidney disease (AOR, 0.48; 95% CI, 0.35–0.66) was associated with decreased use. Bisphosphonate use was associated with reduced mortality (hazard ratio, 0.70; 95% CI, 0.56–0.88). Conclusions: Although bisphosphonate use is recommended for all patients with newly diagnosed MM receiving antineoplastic therapy, 45% of patients in the United States did not receive this guideline-recommended care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".