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Record W2257341667 · doi:10.1016/j.bbmt.2015.09.023

Reply to “Pharmacokinetic and Analytical Issues in Busulfan Area Under the Curve Estimation and Simulation”

2015· letter· en· W2257341667 on OpenAlexaffabout
L. Lee Dupuis, J Zao, Tal Schechter

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2015
Typeletter
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsBusulfanMedicineDosingTherapeutic drug monitoringChromatographyPharmacokineticsHematopoietic stem cell transplantationPharmacologyInternal medicineTransplantationChemistry

Abstract

fetched live from OpenAlex

We thank Phillipe et al. for creating this opportunity to clarify the methods of busulfan concentration determination and area under the curve (AUC) calculation used in our evaluation of the performance of pediatric guidelines for initial busulfan dosing [1]. As stated in our article, plasma busulfan concentrations were assayed using gas chromatography [2,3] by the Department of Laboratory Medicine at The Hospital for Sick Children. Our laboratory participates in external proficiency testing organized by Otsuka Canada Pharmaceutical Inc.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.144
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.144
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0040.005
Open science0.0040.003
Research integrity0.0300.046
Insufficient payload (model declined to judge)0.0090.009

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.

Opus teacher head0.063
GPT teacher head0.381
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2015
Admission routes2
Has abstractyes

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