Identification of treatment effect modifiers and prognostic factors in newly diagnosed and relapsed or refractory multiple myeloma
Bibliographic record
Abstract
Aim: We aimed to identify variables that affect the prognosis and treatment effect in multiple myeloma (MM). Materials & methods: Published literature of randomized controlled trials (RCTs) and population-adjusted indirect comparisons (PAICs) in newly diagnosed (ND) and relapsed/refractory (RR) MM populations reporting overall survival (OS) and progression-free survival (PFS) were identified. Possible treatment effect modifiers (TEMs) were evaluated based on the ratio between effect estimates of different strata within subgroups for OS and PFS among eligible RCTs. Potential prognostic factors (PFs) were identified using the lists of covariates adjusted for in PAICs meeting the eligibility criteria. Results: Sixty-five RCTs and 59 PAICs were included for synthesis. In ND-MM and RR-MM patients, age, sex, International Staging System stage, and cytogenetics were identified as potential TEMs for PFS based on data from published RCTs. Refractory disease, prior therapy exposure status and creatinine clearance were additional TEMs for PFS in RR-MM patients. Eastern Cooperative Oncology Group performance score and creatinine clearance were TEM candidates of PFS for ND-MM stem-cell transplant-ineligible patients. No consistent TEMs for OS were identified across all MM populations. Commonly adjusted variables for both OS and PFS in published PAICs of all populations aligned with potential TEMs of PFS identified in published RCTs. Additionally, subtype of MM, time since diagnosis and extramedullary disease or presence of plasmacytoma were common variables for adjustment in PAICs evaluating RR-MM. Frequency of each variable adjusted for differs by population and outcome. Only one PAIC reported TEMs separately from PFs. Conclusion: TEMs and PFs identified herein can help inform future clinical trial design and serve as a primer when conducting PAICs evaluating OS and PFS in ND/RR-MM.
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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 | 0.046 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".