Rabbit Antithymocyte Globulin Serum Levels: Factors Impacting the Levels and Clinical Outcomes Impacted by the Levels
Bibliographic record
Abstract
Antithymocyte globulin (ATG) levels and clearance vary significantly among patients receiving the same weight-based dose of ATG. To date, ATG area under the curve (AUC), its determinants, and its impact on clinical outcomes have been examined in pediatric hematopoietic cell transplant (HCT) and adult nonmyeloablative HCT. Here we set out to examine ATG AUC in 219 uniformly treated adults undergoing myeloablative allogeneic HCT at our institution. Sera were collected for the determination of pre- or post-HCT ATG AUC. The lowest quintiles of pre- and post-HCT AUC were associated with inferior chronic graft-versus-host disease (GVHD) and relapse-free survival (cGRFS) and a higher risk of acute GVHD, respectively. The highest pre- or post-HCT ATG AUC quintiles were not associated with risk of death, nonrelapse mortality, or relapse. Factors most strongly associated with AUC were day -2 recipient absolute lymphocyte count, body mass index (BMI), and graft lymphocyte content. To achieve ideal pre-HCT AUC (avoiding low AUC to maximize cGRFS) in this HCT setting, ATG dosing will need to take into consideration recipient weight, BMI, and blood and graft lymphocyte counts. Further studies are required to develop a modern ATG dosing schema and to demonstrate that adjusting ATG dose to target a particular AUC is feasible and leads to improved outcomes.
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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 | 0.000 | 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 | 0.000 |
| 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".