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Antinociceptive effects of epidural buprenorphine or medetomidine, or the combination, in conscious cats

2009· article· en· W1996740819 on OpenAlexaff
Paulo V. Steagall, Valerie M Millette, Fernanda Mantovani, Peter Gilbert, Stélio Pacca Loureiro Luna, Tanya Duke‐Novakovski

Bibliographic record

VenueJournal of Veterinary Pharmacology and Therapeutics · 2009
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedetomidineAnesthesiaBuprenorphineMedicineCATSNociceptionConfidence intervalForelimbBlood pressureInternal medicineOpioidHeart rateAnatomy

Abstract

fetched live from OpenAlex

The aim of this study was to compare the antinociceptive effects of epidural buprenorphine (EB), epidural medetomidine (EM) or epidural buprenorphine-medetomidine (EBM). Eight cats were studied. Thermal thresholds (TT) were measured by increasing the temperature of a probe placed on the thorax. Mechanical thresholds (MT) were measured through inflation of a modified blood pressure bladder to the cat's forelimb. After baseline measurements, EB (0.02 mg/kg), EM (0.01 mg/kg) or half of the doses of each drug (EBM) were administered. Data were analysed using anova (P < 0.05) and 95% confidence interval (CI). TT increased from 30 min to 1 h after EB and at 45 min after EM. MT increased from 45 min to 2 h after EB, from 15 min to 1 h after EM and at 30, 45 min and at 2 h after EBM. MT were significantly lower after EB than EM at 30 min. TT were above the upper 95%CI from 15 min to 24 h after EB, from 15 min to 4 h after EM and from 15 min to 8 h after EBM. MT were above the upper 95%CI from 15 min to 5 h, and at 8, 12 and 24 h after EB, from 15 min to 6 h after EM and from 15 min to 6 h and at 12 and 24 h after EBM. All treatments had similar onset. Overall, EB presented longer period of action than EBM and EM. The same magnitude of analgesia was achieved, but with fewer side effects when EBM was compared with EM.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.064
GPT teacher head0.386
Teacher spread0.322 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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