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Record W1541400329

Comparison of buprenorphine and meloxicam for postsurgical analgesia in rats: effects on body weight, locomotor activity, and hemodynamic parameters.

2010· article· en· W1541400329 on OpenAlexaff
Stephane L. Bourque, Michael A. Adams, Kanji Nakatsu, Andrew Winterborn

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsQueen's University
Fundersnot available
KeywordsMeloxicamBuprenorphineMedicineAnesthesiaAnalgesicMean arterial pressureHeart rateHemodynamicsNociceptionBlood pressureOpioidInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Buprenorphine is administered to humans and animals for postoperative pain management, although its use is associated with complications. Alternative analgesics, including the nonsteroidal antiinflammatory meloxicam, are available, but information on their postoperative effects is limited. The objective of the present study was to compare buprenorphine (0.03 mg/kg SC twice daily for 3 d) with meloxicam (2 mg/kg SC initial dose followed by 1 mg/kg SC once daily for 2 d) by assessing parameters relating to postsurgical recovery in rats that underwent surgical implantation of radiotelemetric transducers. Rats treated after surgery with buprenorphine showed greater reductions in body weight, food consumption, locomotor activity, and nighttime heart rates than did meloxicam-treated rats. Buprenorphine and meloxicam treatments both had stimulatory effects on mean arterial pressure and daytime heart rate measurements, although effects on nighttime mean arterial pressure were greater in the buprenorphine-treated rats. In summary, the lesser physiologic changes associated with meloxicam, as compared with buprenorphine, suggest that meloxicam offers advantages for use as a postoperative analgesic after laparotomy and radiotelemetric transducer implantation in rats.

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 score0.775
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.034
GPT teacher head0.325
Teacher spread0.291 · 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

Citations46
Published2010
Admission routes1
Has abstractyes

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