The EMMY longitudinal, cohort study: real-world data to describe multiple myeloma management and outcomes as more therapeutic options emerge
Bibliographic record
Abstract
The therapeutic management of patients with multiple myeloma (MM) is complex. Despite substantial advances, MM remains incurable, and management involves cycles of treatment response, disease relapse, and further therapy. Currently, evidence to support the therapeutic decision is limited. Thus, the EMMY longitudinal, real-world study was designed to annually assess therapeutic management of MM in France to provide evidence to support physicians. During an annual prespecified 3-month recruitment period, eligible patients will be identified from their medical records. Adults aged ≥18 years diagnosed with symptomatic MM and requiring systemic treatment will be eligible. The primary objective, the evolution of MM therapeutic management, will be described, as well as the impact on the following outcomes: time-to-next treatment (TTNT), progression-free survival (PFS), and overall survival (OS). The study plans to recruit 5000 patients over 6 years: 700 to 900 patients annually. EMMY is a unique opportunity to collect real-world data to describe the evolving MM therapeutic landscape and record outcomes in France. These data will provide annual snapshots of various aspects of MM management. This knowledge will provide physicians with real-life, evidence-based data for therapeutic decision-making and ultimately improve treatment for MM 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".