Endothelial activation and stress index ( <scp>EASIX</scp> ) as a biomarker to predict ruxolitinib failure for steroid‐refractory acute graft‐versus‐host disease treatment
Bibliographic record
Abstract
Steroid-refractory acute graft-versus-host disease (SR-aGvHD) remains a significant challenge after haematopoietic cell transplantation (HCT). While ruxolitinib (RUX) has shown efficacy for SR-aGvHD, failure predictors are poorly defined. We assessed 78 patients from six Canadian centres who received RUX for SR-aGvHD. Failure-free survival (FFS) was the primary end-point. Endothelial activation and stress index (EASIX) scores were calculated at defined time points, dichotomized using recursive partitioning. A risk score for RUX failure was developed based on key variables. At RUX initiation, 76% of patients had grade 3-4 aGvHD. The best overall response rate was 75%; 58% experienced RUX failure, while the 6-month FFS was 25.7%. EASIX gradually increased over time even after RUX (p < 0.001). Patients with high EASIX at RUX ≥1.11 had a lower FFS at 6 months (17%) than the patients with low EASIX (54%) (hazard ratio [HR] 2.75 [95%CI 1.23-6.15], p = 0.014). A three-factor RUX failure risk score-gut aGvHD, grade 4 aGvHD and high EASIX at RUX start-stratified patients by 6-month FFS rates: 85.7%, 28.0% and 11.0% for score 0 (n = 28), 1 (n = 25) and ≥2 (n = 34) (HR 2.25[1.42-3.55], p < 0.001). High EASIX can predict the risk of RUX failure in addition to gut aGvHD and grade 4 aGvHD. We propose to add other therapeutic interventions to RUX therapy pre-emptively to high EASIX patients.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".