A simple prognostic system in patients with myelofibrosis undergoing allogeneic stem cell transplantation: a CIBMTR/EBMT analysis
Bibliographic record
Abstract
To develop a prognostic model for patients undergoing allogeneic hematopoietic cell transplantation (allo-HCT) for myelofibrosis (MF), we examined the data of 623 patients undergoing allo-HCT between 2000 and 2016 in the United States (the Center for International Blood and Marrow Transplant Research [CIBMTR] cohort). A Cox multivariable model was used to identify factors prognostic of mortality. A weighted score using these factors was assigned to patients who received transplantation in Europe (the European Bone Marrow Transplant [EBMT] cohort; n = 623). Patient age >50 years (hazard ratio [HR], 1.39; 95% confidence interval [CI], 0.98-1.96), and HLA-matched unrelated donor (HR, 1.29; 95% CI, 0.98-1.7) were associated with an increased hazard of death and were assigned 1 point. Hemoglobin levels <100 g/L at time of transplantation (HR, 1.63; 95% CI, 1.2-2.19) and a mismatched unrelated donor (HR, 1.78; 95% CI, 1.25-2.52) were assigned 2 points. The 3-year overall survival (OS) in patients with a low (1-2 points), intermediate (3-4 points), and high score (5 points) were 69% (95% CI, 61-76), 51% (95% CI, 46-56.4), and 34% (95% CI, 21-49), respectively (P < .001). Increasing score was predictive of increased transplant-related mortality (TRM; P = .0017) but not of relapse (P = .12). The derived score was predictive of OS (P < .001) and TRM (P = .002) but not of relapse (P = .17) in the EBMT cohort as well. The proposed system was prognostic of survival in 2 large cohorts, CIBMTR and EBMT, and can easily be applied by clinicians consulting patients with MF about the transplantation outcomes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".