Prognostic Factors in 448 Newly Diagnosed Multiple Myeloma Receiving Bortezomib-based Induction: Impact of ASCT, Transplant Refusal and High-Risk MM
Bibliographic record
Abstract
Abstract In Hong Kong, newly diagnosed multiple myeloma (NDMM) receives bortezomib-based triplet induction. Upfront autologous stem cell transplant (ASCT) is offered to transplant eligible (TE) patients (NDMM ≤65 years of age), unless medically unfit (TE-unfit) or refused (TE-refused). Data was retrieved for 448 patients to assess outcomes. For the entire cohort, multivariate analysis showed that male gender (p=0.011), international staging system (ISS) 3 (p=0.001), high lactate dehydrogenase (LDH) (p=0.000009) were adverse predictors for overall survival (OS), while complete response/ near complete response (CR/nCR) post-induction (p=0.000078) and ASCT (p=0.000478) were favorable factors for OS. In TE group, upfront ASCT was conducted in 252 (76.1%). Failure to undergo ASCT in TE patients rendered an inferior OS (TE-unfit p=1.06x10-8, TE-refused p=0.002) and event free survival (EFS) (TE-unfit p=0.00013, TE-refused p=0.002). Among TE patients with ASCT, multivariate analysis showed that age≥60 (p=0.001), ISS 3 (p=0.004) and high LDH (p=0.000251) were adverse factors for OS. In those with high-risk features (HR cytogenetics, ISS3, R-ISS 3), ASCT appeared to mitigate their adverse impact. Our data reaffirmed the importance of ASCT. The poor survival inherent with refusal of ASCT should be recognized by clinicians. Finally, improved outcome with ASCT in those with high risk features warrant further studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".