Early Relapse in Transplant-Eligible MM Patients Undergoing Auto-SCT Followed By Maintenance Therapy
Bibliographic record
Abstract
Abstract Introduction Autologous Stem Cell transplantation (auto-SCT), still remains as the standard therapy offered to patients with MM deemed to be eligible for this strategy. Unfortunately, even with the advent of novel and efficacious approaches such as new upfront induction regimens, consolidation and maintenance, patients will progress. Based on the above mentioned, we aimed to assess the impact of maintenance on Early Relapse and its association with predictor factors of survival. Methods All consecutive patients who underwent single auto-SCT at Tom Baker Cancer Center from 01/06 to 03/16 were evaluated. ER was defined as per recent publications ( 24 months (58 months and non-reached, respectively, p=0.0001, Fig 1) In conclusion, patients with ER after auto-SCT remain to be a challenge. Even with the advent of novel agents in the setting of maintenance, patients with ER had poor outcomes. ER for patients treated with maintenance therapy should be characterized better. ER defined as Download : Download high-res image (108KB) Download : Download full-size image Disclosures Jimenez-Zepeda: Amgen: Honoraria; Takeda: Honoraria; Janssen: Honoraria; Celgene: Honoraria. Neri: Janssen: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding. Bahlis: Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".