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Record W1516877588

Effects of 2 different medetomidine infusion rates on selected neurohormonal and metabolic parameters in dogs.

2012· article· en· W1516877588 on OpenAlexaff
Leigh A. Lamont, Shelley Burton, Deanne Caines, Elmabrok Masaoud, Éric Troncy

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsEpinephrineMedetomidineNorepinephrineSalineBolus (digestion)MedicineEndocrinologyInsulinInternal medicineAnesthesiaCatecholamineHeart rateBlood pressureDopamine
DOInot available

Abstract

fetched live from OpenAlex

The effects of 2 different 8-hour continuous rate infusions (CRIs) of medetomidine on epinephrine, norepinephrine, cortisol, glucose, and insulin levels were investigated in 6 healthy dogs. Each dog received both treatments and a control as follows: MED1 = 2 μg/kg bodyweight (BW) loading dose followed by 1 μg/kg BW per hour CRI; MED2 = 4 μg/kg BW loading dose followed by 2 μg/kg BW per hour CRI; and CONTROL = saline bolus followed by a saline CRI. Both infusion rates of medetomidine decreased norepinephrine levels throughout the infusion compared to CONTROL. While norepinephrine levels tended to be lower with the MED2 treatment compared to the MED1, this difference was not significant. No differences in epinephrine, cortisol, glucose, or insulin were documented among any of the treatments at any time point. At the low doses used in this study, both CRIs of medetomidine decreased norepinephrine levels over the 8-hour infusion period, while no effects were observed on epinephrine, cortisol, glucose, and insulin.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.035
GPT teacher head0.278
Teacher spread0.243 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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