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Record W4392352459 · doi:10.3389/fvets.2024.1349590

Safety study of cannabidiol products in healthy dogs

2024· article· en· W4392352459 on OpenAlexaff
William Bookout, Margitta Dziwenka, Kaiti Valm, Jennifer Kovacs-Nolan

Bibliographic record

VenueFrontiers in Veterinary Science · 2024
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsNutrasource
Fundersnot available
KeywordsCannabidiolTolerabilityMedicineAdverse effectDosingBroad spectrumRandomized controlled trialBody weightAnimal scienceInternal medicineCannabisBiologyChemistry

Abstract

fetched live from OpenAlex

The tolerability of different cannabinoids given orally to dogs was evaluated in a randomized, non-blinded, negative controlled, parallel design 90-day repeat dose study with a 14-day recovery period. Healthy beagles (16 males and 16 females) were randomized into four treatment groups and treated with either medium chain triglyceride oil as the control or one of the following: broad spectrum cannabidiol, broad spectrum cannabidiol with cannabigerol, or broad spectrum cannabidiol with cannabidiolic acid at 5 mg total cannabinoids/kg body weight/day. Animals were observed daily with detailed clinical examinations conducted weekly. Animals were monitored for an additional 2 weeks after dosing. Body weights, food consumption and clinical pathology evaluations were included in the study. Cannabinoids were well tolerated when healthy male and female beagles were dosed for 90 consecutive days. Annual post-market surveillance data for hemp-derived supplement products sold for use in dogs from 2010 to 2023 (partial year) shows that the rate per 1 million administrations sold is 2.10 for adverse events and 0.01 for serious adverse events. Based on the results of this study, other published studies, and data from extensive post-market surveillance, hemp-derived cannabinoids are well tolerated in healthy dogs at a dose of 5 mg/kg body weight/day.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.342
Teacher spread0.311 · 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 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

Citations13
Published2024
Admission routes1
Has abstractyes

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