Ranking the Importance of Prognostic Factors for Relapsed/Refractory Multiple Myeloma: International Physician Panel Consensus Following a Systematic Literature Review
Bibliographic record
Abstract
PURPOSE: The increasing use of single-arm and nonrandomized trial designs in oncology aims to expedite patient access to novel treatments. To contextualize their results using strategies such as external control arms or indirect treatment comparisons, prespecification of, and subsequent adjustment for, prognostic factors is required to ensure comparability of populations and avoid bias. This study aimed to systematically identify and rank prognostic factors relevant to treatment outcomes in patients with relapsed/refractory multiple myeloma (RRMM). MATERIALS AND METHODS: To comprehensively identify prognostic factors, a systematic literature review was conducted with databases searched between January 2016 and April 2022. Clinical studies enrolling adult patients with RRMM and assessing prognostic significance using adjusted analyses were included. Subsequently, an international panel of multiple myeloma experts confirmed and ranked these variables by their importance in predicting clinical outcomes. A structured series of expert consultations was conducted from November 2022 to February 2023, including 2 rounds of consensus meetings. RESULTS: Of 125 studies included in the systematic literature review, 112 described 97 factors significantly associated with at least 1 outcome of interest. A total of 25 factors associated with overall survival and/or objective response and reported in at least 2 studies were included in the ranking process. The physician panel unanimously agreed on the 6 most important prognostic factors: cytogenetic risk, age, refractory status, disease stage, performance status, and extramedullary disease/plasmacytoma. CONCLUSION: This list of RRMM prognostic factors can be used in comparative analyses to assess treatment effectiveness in the absence of head-to-head trials.
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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 | 0.096 | 0.232 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.032 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".