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Emesis in dogs: a review

2009· review· en· W2069598518 on OpenAlexaff
Clive Elwood, Patrick Devauchelle, Jonathan Elliott, Valérie Freiche, Alexander J. German, M. Gualtieri, Edward J. Hall, E. den Hertog, Reto Neiger, Dominique Peeters, Xavier Roura, Karine Savary-Bataille

Bibliographic record

VenueJournal of Small Animal Practice · 2009
Typereview
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
FundersKU Leuven
KeywordsMedicineVomitingModalitiesIntensive care medicineClinical PracticeSign (mathematics)AnesthesiaFamily medicine

Abstract

fetched live from OpenAlex

Emesis is a common presenting sign in small animal practice. It requires a rational approach to management that is based upon a sound understanding of pathophysiology combined with logical decision making. This review, which assesses the weight of available evidence, outlines the physiology of the vomiting reflex, causes of emesis, the consequences of emesis and the approach to clinical management of the vomiting dog. The applicability of diagnostic testing modalities and the merit of traditional approaches to management, such as dietary changes, are discussed. The role and usefulness of both traditional and novel anti-emetic drugs is examined, including in specific circumstances such as following cytotoxic drug treatment. The review also examines areas in which common clinical practice is not necessarily supported by objective evidence and, as such, highlights questions worthy of further clinical research.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.213
GPT teacher head0.464
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations89
Published2009
Admission routes1
Has abstractyes

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