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Record W4401046930 · doi:10.1016/j.tvjl.2024.106208

European Network for Optimization of Veterinary Antimicrobial Therapy (ENOVAT) guidelines for antimicrobial use in canine acute diarrhoea

2024· article· en· W4401046930 on OpenAlexaff
Lisbeth Rem Jessen, Melanie Werner, David Singleton, C. Prior, Farid Foroutan, Aude Ferran, Carolina Arenas, Charlotte Reinhard Bjørnvad, Eran Lavy, Fergus Allerton, K. Allensbach, Luca Guardabassi, Stefan Unterer, T. Bodnárová, Ulrika Windahl, Marnie Brennan, J. Scott Weese, Karolina Scahill

Bibliographic record

VenueThe Veterinary Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsUniversity of GuelphMcMaster University
FundersRoyal CaninEuropean Cooperation in Science and TechnologyBritish Small Animal Veterinary Association
KeywordsGuidelineMedicineMedical prescriptionGrading (engineering)AntimicrobialIntensive care medicineMultidisciplinary approachFamily medicinePathologyPharmacologyPolitical science

Abstract

fetched live from OpenAlex

Acute diarrhoea is a common presentation in dogs, and a common reason for antimicrobial prescription and nutraceutical use. This evidence-based guideline provides recommendations for antimicrobial and probiotic treatment of canine acute diarrhoea (CAD). A multidisciplinary panel developed the recommendations by adhering to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. The opinions of stakeholders (general veterinary practitioners and dog owners) were collected and incorporated to ensure the applicability of this guideline. Four strong recommendations informed by high certainty evidence, and three conditional recommendations informed by very low or low certainty evidence, were drafted by the panel, along with an ungraded section on diagnostic work-up of dogs with acute diarrhoea. The ENOVAT guidelines initiative encourages national or regional guideline makers to use the evidence presented in this document, and the supporting systematic review, to draft national or local guidance documents.

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 score0.694
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.151
GPT teacher head0.386
Teacher spread0.234 · 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

Citations21
Published2024
Admission routes1
Has abstractyes

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