IPSS Poor-Risk Karyotype as a Predictor of Outcome for Patients with Myelodysplastic Syndrome following Myeloablative Stem Cell Transplantation
Bibliographic record
Abstract
The optimal therapy for myelodysplastic syndrome (MDS) is allogeneic bone marrow (BM) or blood (BSC) stem cell transplantation (SCT), although outcomes are limited by nonrelapse mortality (NRM) and relapse. A retrospective review was performed of 156 patients who underwent SCT (114 BM, 42 BSC) for MDS or secondary acute myelogenous leukemia (sAML) at our institution. Fifty-five patients remain in continuous complete remission: 35 BM recipients and 20 BSC recipients (median follow-up 139 and 89 months, respectively). Estimated 7-year event-free survival (EFS), NRM, and risk of relapse (ROR) are 33% (95% confidence intervals [CI] 25%-43%), 42% (CI 33%-51%), and 25% (CI 17%-33%) for the BM cohort and 45% (CI 32%-64%, P= .07), 32% (CI 18%-47%, P= .15), and 23% (CI 11%-37%, P= .79) for the BSC cohort. Multivariate analysis showed IPSS poor-risk cytogenetics (P< .001), time from diagnosis to SCT (P< .001), FAB subgroup (P= .001), recipients not in complete remission (CR1) at SCT (P= .005), and the development of acute graft-versus-host disease (aGVHD) (P= .04) were all predictive of an inferior EFS. The FAB subgroup (P= .002), poor-risk karyotype (P= .004), and non-CR1 status also correlated with ROR in multivariate analysis. EFS for poor-risk karyotype patients was superior after receiving BSC compared to BM (39% versus 6%, P< .001). SCT outcomes in MDS/sAML are strongly associated with the IPSS cytogenetic risk group, although the use of BSC in poor-risk karyotype patients may lead to a more favorable long-term EFS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".