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Record W3045798300 · doi:10.1111/jvp.12893

Pharmacokinetics of acetaminophen after intravenous and oral administration in fasted and fed Labrador Retriever dogs

2020· article· en· W3045798300 on OpenAlexaboutno aff
Irene Sartini, Beata Łebkowska‐Wieruszewska, A. Lisowski, Amnart Poapolathep, B. Cuniberti, Mario Gıorgı

Bibliographic record

VenueJournal of Veterinary Pharmacology and Therapeutics · 2020
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsnot available
FundersUniversità di Pisa
KeywordsAcetaminophenPharmacokineticsBioavailabilityLabrador RetrieverPlasma concentrationMedicineOral administrationAnesthesiaPharmacologySurgery

Abstract

fetched live from OpenAlex

Acetaminophen (paracetamol) is used in dogs to manage fever and mild pain. The aim of this study was to assess the pharmacokinetics of acetaminophen in both fed and fasted Labrador Retrievers after a single intravenous and oral administration (20 mg/kg). Six healthy dogs underwent three treatments in a randomized block study (a, n = 2; b, n = 2; c, n = 2). In phase one, group a received acetaminophen intravenously, group b and c orally after being fasted and fed, respectively. In phase two and three, groups were swapped, and the experiment was repeated. At the end of the trial, each dog received the same treatment. Acetaminophen plasma concentrations were detected using a validated HPLC-UV method. The pharmacokinetic analysis was performed using a noncompartmental model. Clearance, volume at steady state and half-life of acetaminophen in Labrador Retrievers were 0.42 L/kg hr, 0.87 L/kg and 1.35 hr, respectively. No significant statistical differences were found between fasted and fed dogs regarding maximum plasma concentration, time at maximum concentration and bioavailability as measured by the AUC. Feeding does not significantly affect the acetaminophen oral pharmacokinetics.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.123
GPT teacher head0.405
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations19
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Pharmacology and TherapeuticsSame topicDrug-Induced Hepatotoxicity and ProtectionFrench-language works237,207