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Record W2225960731 · doi:10.11575/prism/35607

Hemodynamic Effects of an Intravenous Infusion of Medetomidine at Six Different Dose Regimens in Isoflurane-Anesthetized Dogs

2010· article· en· W2225960731 on OpenAlexafffund
Johanna Kaartinen, Daniel Pang, Maxim Moreau, Outi Vainio, Francis Beaudry, Jérôme R. E. del Castillo, Leigh A. Lamont, Sophie Cuvelliez, Éric Troncy

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Sedative Agents
Canadian institutionsCegep de Saint Hyacinthe
FundersNatural Sciences and Engineering Research Council of CanadaPfizer CanadaUniversité de MontréalAcadémie de Médecine Vétérinaire du QuébecPfizer
KeywordsMedicineHeart rateMedetomidineIsofluraneAnesthesiaHemodynamicsBlood pressureMean arterial pressureCardiac indexLoading doseCardiac outputInternal medicine

Abstract

fetched live from OpenAlex

This study investigated the dose dependency of the hemodynamic effects of IV medetomidine (MED) constant-rate infusion (CRI) during isoflurane anesthesia. Twenty-four healthy beagles randomly received one of six MED CRI regimens. A loading dose of MED was administered IV at 0.2, 0.5, 1.0, 1.7, 4.0, or 12.0 ug/kg-1 for 10 minutes, followed by a maintenance CRI providing identical dose amounts over 60 minutes. Heart rate and mean arterial blood pressure were recorded, blood gases were analyzed, and cardiac index (CI) was determined. Statistical analysis involved a repeated measures linear model. Baseline CI demonstrated a dose-dependent decrease as the MED dose increased, with decreases of 14.9% (SD, 12.7%), 21.7% (17.9%), 27.1% (13.2%), 44.2% (9.7%), 47.9% (8.1%), and 61.2% (14.1%) at doses of 0.2, 0.5, 1.0, 1.7, 4.0, and 12.0 ug/kg-1, respectively. The four lowest doses induced limited and transient changes in heart rate, mean arterial pressure, and CI. Further investigation into potential perioperative uses of MED CRI is warranted.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations21
Published2010
Admission routes2
Has abstractyes

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