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Record W3157957845 · doi:10.1002/jcph.1876

Longitudinal Impact of Acute Spinal Cord Injury on Clinical Pharmacokinetics of Riluzole, a Potential Neuroprotective Agent

2021· article· en· W3157957845 on OpenAlexaff
Ashley Nguyen, Diana S‐L Chow, Lei Wu, Yang Teng, Mahua Sarkar, Elizabeth G. Toups, James S. Harrop, Karl M. Schmitt, Michele M. Johnson, James D. Guest, Bizhan Aarabi, Christopher I. Shaffrey, Maxwell Boakye, Ralph F. Frankowski, Michael G. Fehlings, Robert G. Grossman

Bibliographic record

VenueThe Journal of Clinical Pharmacology · 2021
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsRiluzoleMedicinePharmacokineticsNeuroprotectionPharmacologySpinal cord injuryAmyotrophic lateral sclerosisPopulationAnesthesiaSpinal cordInternal medicine

Abstract

fetched live from OpenAlex

Riluzole, a benzothiazole sodium channel blocker that received US Food and Drug Administration approval to attenuate neurodegeneration in amyotrophic lateral sclerosis in 1995, was found to be safe and potentially efficacious in a spinal cord injury (SCI) population, as evident in a phase I clinical trial. The acute and progressive nature of traumatic SCI and the complexity of secondary injury processes can alter the pharmacokinetics of therapeutics. A 1-compartment with first-order elimination population pharmacokinetic model for riluzole incorporating time-dependent clearance and volume of distribution was developed from combined data of the phase 1 and the ongoing phase 2/3 trials. This change in therapeutic exposure may lead to a biased estimate of the exposure-response relationship when evaluating therapeutic effects. With the developed model, a rational, optimal dosing scheme can be designed with time-dependent modification that preserves the required therapeutic exposure of riluzole.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.173
GPT teacher head0.556
Teacher spread0.383 · 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 designObservational
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

Citations7
Published2021
Admission routes1
Has abstractyes

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