Rabbit ATG/ATLG in preventing graft-versus-host disease after allogeneic stem cell transplantation: consensus-based recommendations by an international expert panel
Bibliographic record
Abstract
This collaborative initiative aimed to provide recommendations on the use of polyclonal antithymocyte globulin (ATG) or anti-T lymphocyte globulin (ATLG) for the prevention of graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (HSCT). A comprehensive review of articles released up to October, 2018 was performed as a source of scientific evidence. Fourteen clinically relevant key questions to the domains indication, administration, and post-transplant management were developed and recommendations were produced using the Delphi technique involving a Panel of 14 experts. ATG/ATLG was strongly recommended as part of myeloablative conditioning regimen prior to matched or mismatched unrelated bone marrow or peripheral blood allogeneic HSCT in malignant diseases to prevent severe acute and chronic GvHD. ATG/ATLG was also recommended prior to HLA-identical sibling peripheral HSCT with good but lesser bulk of evidence. In reduced intensity or nonmyeloablative conditioning regimens, ATG/ATLG was deemed appropriate to reduce the incidence of acute and chronic GvHD, but a higher risk of relapse should be taken into account. Recommendations regarding dose, application, and premedication were also provided as well as post-transplant infectious prophylaxis and vaccination. Overall, these recommendations can be used for a proper and safe application of polyclonal ATG/ATLG to prevent GvHD after allogeneic HSCT.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".