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Record W1976804024 · doi:10.1038/clpt.2011.47

Effects of Valproic Acid on Organic Acid Metabolism in Children: A Metabolic Profiling Study

2011· article· en· W1976804024 on OpenAlexaff
Kristin E. Price, Robin E. Pearce, Uttam Garg, Bryce A. Heese, Laurie D. Smith, Janice E. Sullivan, Mary Jayne Kennedy, Jim Bale, Robert M. Ward, Thomas K. H. Chang, F. S. Abbott, J. Steven Leeder

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2011
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsUniversity of British Columbia
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsValproic AcidCarbamazepineMetabolomicsMetabolismPharmacologyOrganic acidMedicinePhysiologyInternal medicineChemistryBioinformaticsBiologyBiochemistryEpilepsyPsychiatry

Abstract

fetched live from OpenAlex

Young children are at increased risk for valproic acid (VPA) hepatotoxicity. Urinary organic acid profiles, as a surrogate of mitochondrial function, were obtained in children 1.9 to 17.3 years of age (n = 52) who were undergoing treatment with VPA for seizure disorders. Age-matched patients receiving treatment with carbamazepine (CBZ; n = 50) and healthy children not undergoing treatment (n = 22) served as controls. Age-related changes in organic acid profiles were observed in all three groups. Although the untreated and CBZ control groups were indistinguishable from each other with respect to the principal-component analysis (PCA) score plots of the subjects, a distinct boundary was apparent between the VPA and each of the control groups. Interindividual variability was observed in the VPA-induced alterations in endogenous pathways corresponding to branched-chain amino acid metabolism and oxidative stress. The data suggest that more detailed metabolomic analysis may provide novel insights into biological mechanisms and predictive biomarkers for children at highest risk for serious toxicity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.403
Teacher spread0.316 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations41
Published2011
Admission routes1
Has abstractyes

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