Improved survival after acute graft- <i>versus</i> -host disease diagnosis in the modern era
Bibliographic record
Abstract
A cute graft-versus-host disease remains a major threat to a successful outcome after allogeneic hematopoietic cell transplantation. While improvements in treatment and supportive care have occurred, it is unknown whether these advances have resulted in improved outcome specifically among those diagnosed with acute graft-versus-host disease. We examined outcome following diagnosis of grade II-IV acute graft-versus-host disease according to time period, and explored effects according to original graft-versus-host disease prophylaxis regimen and maximum overall grade of acute graft-versus-host disease. Between 1999 and 2012, 2,905 patients with acute myeloid leukemia (56%), acute lymphoblastic leukemia (30%) or myelodysplastic syndromes (14%) received a sibling (24%) or unrelated donor (76%) blood (66%) or marrow (34%) transplant and developed grade II-IV acute graft-versus-host disease (n=497 for 1999–2001, n=962 for 2002–2005, n=1,446 for 2006–2010). The median (range) follow-up was 144 (4–174), 97 (4–147) and 60 (8–99) months for 1999–2001, 2002–2005, and 2006–2010, respectively. Among the cohort with grade II-IV acute graft-versus-host disease, there was a decrease in the proportion of grade III-IV disease over time with 56%, 47%, and 37% for 1999–2001, 2002–2005, and 2006–2012, respectively (P
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".