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Record W2758801632

Uses and Doses of Local Anesthetics in Fish, Amphibians, and Reptiles.

2017· article· en· W2758801632 on OpenAlexaff
F. Chatigny, Collins Kamunde, Catherine M. Creighton, Don Stevens

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsFish <Actinopterygii>AmphibianLocal anestheticMedicineAnestheticDiversity of fishBiologyEcologyFisheryAnesthesia
DOInot available

Abstract

fetched live from OpenAlex

Local anesthetics are an integral part of routine pain management in mammals, yet their use is relatively limited in fish, amphibians and reptiles. These animals frequently undergo potentially painful surgical procedures and therefore could possibly benefit from those drugs. Some recommendations are currently available in the literature concerning analgesic use in these animals. However the pharmacological properties, safety and often efficacy of local anesthetic drugs have not been investigated yet in fish, amphibians, or reptiles. This review compiled current information concerning the use of those agents in fish, reptiles and amphibians to help clinicians make an informed decision as to which dose and drug to use. The resulting literature search showed that the literature concerning use of local analgesics in fish and amphibians is very limited while the literature for reptiles is more extensive. We found few experimental studies evaluating the efficacy of local anesthetics. Further studies would provide additional information for developing guidelines to improve the welfare of fish, amphibians and reptiles.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.383

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 head0.079
GPT teacher head0.312
Teacher spread0.233 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

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