Association of Antiepileptic Medications with Outcomes after Allogeneic Hematopoietic Cell Transplantation with Busulfan/Cyclophosphamide Conditioning
Bibliographic record
Abstract
High-dose busulfan (BU) followed by high-dose cyclophosphamide (CY) before allogeneic hematopoietic cell transplantation (HCT) has long been used as treatment for hematologic malignancies. Administration of phenytoin or newer alternative antiepileptic medications (AEMs) prevents seizures caused by BU. Phenytoin induces enzymes that increase exposure to active CY metabolites in vivo, whereas alternative AEMs do not have this effect. Lower exposure to active CY metabolites with the use of alternative AEMs could decrease the risk of toxicity but might increase the risk of recurrent malignancy after HCT. Previous studies have not determined whether outcomes with alternative AEMs differ from those with phenytoin in patients treated with BU/CY before allogeneic HCT. We studied a cohort of 2155 patients, including 1460 treated with phenytoin and 695 treated with alternative AEMs, who received BU/CY before allogeneic HCT between 2004 and 2014. We found no differences suggesting decreased overall survival or relapse-free survival or increased risks of relapse, nonrelapse mortality, acute or chronic graft-versus-host disease, or regimen-related toxicity associated with the use of alternative AEMs compared with phenytoin. The risk of dialysis was lower in the alternative AEM group than in the phenytoin group. Alternative AEMs are safe for prevention of seizures after BU administration and can avoid the undesirable toxicities and drug interactions caused by phenytoin.
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 | 0.000 | 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 | 0.001 |
| 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".