Early liver complications after allogeneic haematopoietic stem cell transplantation in patients with myelofibrosis: A study on behalf of the Chronic Malignancies Working Party of the <scp>EBMT</scp>
Bibliographic record
Abstract
Outcome after HSCT, (A-E): probability of overall survival (OS) by (A) liver AE grade, (B) age at HSCT (years), (C) HCT-CI risk category, (D) type of disease, (E) type of donor and (F-J): cumulative incidence of non-relapse mortality (NRM) by (F) liver AE grade, (G) liver AE + organ involvement, (H) bilirubin toxicity, (I) iatrogenic liver AE (as reported on questionnaire) and (J) type of donor.Figure (A) and (F-I) are landmark analyses starting from 100 days after HSCT, all other figures start from HSCT.Numbers below the graphs show the number at risk.Shaded areas show the 95% confidence intervals.p-values were obtained using the log-rank test from figure (A-E) and Gray's test for Figure (F-J).high, HCT-CI risk score 3 or more; HSCT, haematopoietic stem cell transplantation; Intermediate, HCT CI risk score 1-2; low, HCT-CI risk score 0; MMUD, mismatched unrelated donor; MUD, matched unrelated donor; pMF, primary myelofibrosis; sMF, secondary myelofibrosis; tAML, transformed to AML.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".