Maintenance therapy in transplant ineligible adults with newly‐diagnosed multiple myeloma: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The role of maintenance therapy in transplant ineligible multiple myeloma (MM) patients following a period of fixed duration induction therapy remains unclear. OBJECTIVES: We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) examining maintenance therapy compared to observation. METHODS: We conducted a comprehensive search including MEDLINE, Embase, and the Cochrane database up to February 28, 2020, for RCTs comparing maintenance therapy to observation in newly diagnosed transplant ineligible MM patients. Two authors independently screened studies for eligibility, extracted data, and assessed risk of bias. We performed meta-analyses using a random-effects model and assessed certainty using GRADE methodology. MAIN RESULTS: We included five RCTs with a total of 1139 patients. Patients receiving maintenance therapy had improved progression-free survival (PFS) compared to observation (Hazard ratio [HR] 0.48, 95% confidence interval [CI] 0.38 to 0.62, high certainty); however, there was no difference in overall survival (HR 0.96, 95% CI 0.76-1.2, moderate certainty). Adverse events were higher in the maintenance group compared to observation (very low to moderate certainty). CONCLUSION: Maintenance therapy increases PFS in transplant ineligible MM patients following a fixed period of induction therapy; however, this must be weighed against the increased risk of adverse events.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".