Patient age and donor <scp>HLA</scp> matching can stratify allogeneic hematopoietic cell transplantation patients into prognostic groups
Bibliographic record
Abstract
BACKGROUND: Mixed results surround the accuracy of commonly used prognostic risk scores to predict overall survival (OS) and non-relapse mortality (NRM) in allogeneic hematopoietic stem cell transplant (allo-HCT) recipients. We hypothesize that a simple prognostic score performs better than conventional scoring systems. PATIENTS AND METHODS: OS risk factors, HCT-CI, age-HCT-CI, and augmented-HCT-CI were studied in 299 patients who underwent allo-HCT for myeloid and lymphoid malignancies. A scoring system was developed based on results and validated in a different cohort of 455 patients. RESULTS: Two-year OS was 51% (95% confidence interval (CI) 0.45-0.56); 2-year NRM was 34% (95% CI 0.29-0.39). HCT-CI and associated scores were grouped into 0-2 and ≥3. Age and HLA mismatch status were the only risk factors to affect OS in multivariate analysis (p = 0.02 and 0.05, respectively). HCT-CI and associated scores were not informative for OS prediction. The weighted scoring system assigned 0 to 2 points for age < 50, 50-64, or ≥65, respectively, and 0-1 points for no HLA mismatch versus any mismatch (except HLA-DQ). Distinct 2-year OS (62%, 53%, and 38% [p = <0.001]) and NRM (24%, 34%, and 43% [p = 0.02]) groups were characterized. The scoring system was validated in a second independent cohort with similar results on OS and NRM (p < 0.001). CONCLUSIONS: A simple scoring system based on recipient's age and mismatch status accurately predict OS and NRM in two distinct cohorts of allo-HCT patients. Its simplicity makes it a helpful tool to aid clinicians and patients in clinical decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".