A Personalized Prediction Model for Outcomes after Allogeneic Hematopoietic Cell Transplant in Patients with Myelodysplastic Syndromes
Bibliographic record
Abstract
Allogeneic hematopoietic stem cell transplantation (HCT) remains the only potentially curative option for myelodysplastic syndromes (MDS). Mortality after HCT is high, with deaths related to relapse or transplant-related complications. Thus, identifying patients who may or may not benefit from HCT is clinically important. We identified 1514 patients with MDS enrolled in the Center for International Blood and Marrow Transplant Research Registry and had their peripheral blood samples sequenced for the presence of 129 commonly mutated genes in myeloid malignancies. A random survival forest algorithm was used to build the model, and the accuracy of the proposed model was assessed by concordance index. The median age of the entire cohort was 59 years. The most commonly mutated genes were ASXL1(20%), TP53 (19%), DNMT3A (15%), and TET2 (12%). The algorithm identified the following variables prior to HCT that impacted overall survival: age, TP53 mutations, absolute neutrophils count, cytogenetics per International Prognostic Scoring System-Revised, Karnofsky performance status, conditioning regimen, donor age, WBC count, hemoglobin, diagnosis of therapy-related MDS, peripheral blast percentage, mutations in RAS pathway, JAK2 mutation, number of mutations/sample, ZRSR2, and CUX1 mutations. Different variables impacted the risk of relapse post-transplant. The new model can provide survival probability at different time points that are specific (personalized) for a given patient based on the clinical and mutational variables that are listed above. The outcomes' probability at different time points may aid physicians and patients in their decision regarding HCT.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".