Toxicity analysis of busulfan pharmacokinetic therapeutic dose monitoring
Bibliographic record
Abstract
Busulfan-based conditioning regimens are associated with serious toxicities and literature reports increased risk of toxicities when daily area under the curve concentrations exceed 6000 µM-minute. We implemented real time pharmacokinetic-guided therapeutic drug monitoring of busulfan for myeloablative conditioning regimens. The objective was to compare toxicity of intravenous busulfan before and after therapeutic drug monitoring implementation. The primary endpoint was incidence of hepatotoxicity. Medical records were retrospectively reviewed with weight-based dose Busulfan/Cyclophosphamide (BuCy) conditioning from August 2017 through March 2018 ( N = 14) and therapeutic drug monitoring from April 2018 through December 2018 ( N = 22). Recipients of busulfan therapeutic drug monitoring were younger than those receiving weight-based dose (median: 45 vs. 58 years, p = 0.008). No other baseline differences were observed. There was no difference in hepatotoxicity between therapeutic drug monitoring and weight-based dose (median 1 vs. 0 days, p = 0.40). In the therapeutic drug monitoring group, 45% of patients had increases and 41% had decreases in busulfan dose after Bu1. Repeat pharmacokinetic after Bu2 were required in 32% of patients. A pharmacokinetic dose monitoring program for myeloablative conditioning intravenous busulfan regimens may be considered a safe practice in stem cell transplant recipients. The majority of patients receiving pharmacokinetic-guided therapeutic drug monitoring required dose changes and therapeutic drug monitoring patients had no significant difference in toxicity compared to those receiving weight-based dose.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".