IV Busulfan Dose Individualization in Children undergoing Hematopoietic Stem Cell Transplant: Limited Sampling Strategies
Bibliographic record
Abstract
We currently calculate area under the busulfan concentration time curve (AUC) using 7 plasma busulfan concentrations (AUC7) drawn after the first of 16 i.v. busulfan doses given as a 2-hour infusion every 6 hours. The aim of this study was to develop and validate limited sampling strategies (LSSs) using 3 or fewer busulfan concentration values with which to reliably calculate AUC in children undergoing hematopoietic stem cell transplant (HSCT). Children in the development group (44) received i.v. busulfan at Sick Kids; the validation group consisted of 35 children who received care at CHU Ste-Justine. Busulfan doses given and subsequent plasma busulfan concentrations were recorded. LSSs using 1 to 3 concentration-time points were developed using multiple linear regression. LSS were considered to be acceptable when adjusted r(2) > 0.9, mean bias <15% and precision <15%. Extent of agreement between the AUC7 values and the LSS AUC was assessed by the intraclass correlation coefficient (ICC) and Bland-Altman (BA) analysis. Agreement was considered to be excellent when the lower limit of the 95% confidence limit of the ICC exceeded 0.9 and when the limits of agreement in the BA analysis were +/-15% for both AUC and dose. Administration of the theoretic adjusted busulfan doses based on each LSS was simulated and cases where the resulting AUC was >1500 or <900 microM x min were noted. LSSs using 1, 2, or 3 plasma busulfan concentrations were developed that showed excellent agreement with AUC7 and adjusted busulfan doses. In the validation sample, only the 2- and 3-point LSSs demonstrated acceptable precision and lack of bias. LSSs using 2 or 3 plasma busulfan concentrations can be used to reliably estimate busulfan AUC after IV administration in children undergoing HSCT.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".