Revised International Staging System Is Predictive and Prognostic for Early Relapse (<24 months) after Autologous Transplantation for Newly Diagnosed Multiple Myeloma
Bibliographic record
Abstract
The revised International Staging System (R-ISS) combines ISS with genetic markers and lactate dehydrogenase and can prognosticate newly diagnosed multiple myeloma (MM). Early relapse (<24 months) after upfront autologous hematopoietic cell transplantation (AHCT) strongly predicts inferior overall survival (OS). We examined the ability of R-ISS in predicting early relapse and its independent prognostic effect on postrelapse survival after an early relapse. Using the Center for International Blood and Marrow Transplant Research database we identified MM patients receiving first AHCT within 18 months after diagnosis with available R-ISS stage at diagnosis (n = 628). Relative risks of relapse/progression, progression-free survival (PFS), and OS were calculated with the R-ISS group as a predictor in multivariate analysis. Among early relapsers, postrelapse survival was tested to identify factors affecting postrelapse OS. The cumulative incidence of early relapse was 23%, 39%, and 50% for R-ISS I, R-ISS II, and R-ISS III, respectively (P < .001). Shorter PFS and OS were seen with higher stage R-ISS. R-ISS was independently predictive for inferior postrelapse OS among early relapsers, as was the presence of ≥3 comorbidities and the use of ≥2 induction chemotherapy lines. R-ISS stage at diagnosis predicts early post-AHCT relapse and independently affects postrelapse survival among early relapsers.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".