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Record W2767313310 · doi:10.1111/jsap.12776

Effects of omega‐3 polyunsaturated fatty acids and aspirin, alone and combined, on canine platelet function

2017· article· en· W2767313310 on OpenAlexafffund
Shannon Westgarth, Shauna L. Blois, R. Darren Wood, Adronie Verbrugghe, David W.L.

Bibliographic record

VenueJournal of Small Animal Practice · 2017
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of Guelph
FundersOVC Pet TrustAmerican College of Veterinary Internal Medicine
KeywordsMedicineAspirinFish oilPlateletArachidonic acidEpinephrineWhole bloodRandomized controlled trialAnesthesiaPharmacologyInternal medicineGastroenterologyFish <Actinopterygii>BiochemistryFishery

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare haemostatic function in healthy dogs after treatment with low-dose aspirin alone, fish oil alone or a combination of these two therapies. MATERIALS AND METHODS: Double-blinded randomised controlled clinical trial on 16 healthy client-owned dogs. Comprehensive haemostatic testing was performed at baseline and after 7 days of therapy with low-dose aspirin in all dogs. Following a 14-day washout, six dogs received fish oil, and nine dogs received combination therapy of aspirin plus fish oil; haemostatic testing was performed before and at 7 and 28 days after treatment initiation. RESULTS: Aspirin was associated with significantly decreased platelet function as measured by a collagen-epinephrine cartridge and inhibited arachidonic acid-induced whole-blood platelet aggregometry. Fish oil alone did not significantly affect any haemostatic tests. The combination of aspirin plus fish oil therapy caused a significantly greater inhibition of adenosine diphosphate and collagen-induced whole blood aggregometry compared to aspirin alone. CLINICAL SIGNIFICANCE: Fish oil added to aspirin therapy appears to augment inhibition of some measures of platelet function in healthy dogs.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.313
Teacher spread0.286 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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