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Record W4413370984 · doi:10.1111/vec.70016

Acute Respiratory Distress Syndrome in Veterinary Medicine—The ARDSVet Definitions

2025· article· en· W4413370984 on OpenAlexaff
Anusha Balakrishnan, Deborah C. Silverstein, Daniela Bedenice, Alexa M. Bersenas, John Bourgeois, Christopher L. Carroll, Bettina Dunkel, Thomas D. Greensmith, Kate Hopper, Kara M. Lascola, Nilam S. Mangalmurti, Elizabeth A. Rozanski, Pamela A. Wilkins, Nadir Yehya

Bibliographic record

VenueJournal of Veterinary Emergency and Critical Care · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Guelph
FundersNational Heart, Lung, and Blood InstituteDorothy Russell Havemeyer Foundation
KeywordsMedicineARDSAcute respiratory distressDelphi methodVeterinary medicineConsensus conferenceIntensive care medicineLungInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To use a systematic, evidence-based consensus process to develop updated definitions for acute respiratory distress syndrome (ARDS) in veterinary medicine to facilitate its recognition and diagnosis. DESIGN: International consensus conference series involving 12 multidisciplinary international content experts from three countries, using consensus conference methodology and implementation science. A systematic review of the literature was carried out for ARDS and acute lung injury in veterinary medicine. Updated definitions of ARDS were generated based on synthesis of human and veterinary literature. Consensus on the definitions was achieved through Delphi-style surveys involving the above subject matter experts. Draft recommendations were made available through industry specialty listservs for feedback, which was incorporated in the final definitions. RESULTS: Updated definitions were developed for Veterinary Acute Respiratory Distress Syndrome (ARDSVet) in small animals (dogs and cats) and large animals (equids). For small animals, 690 publications were identified for dogs and 99 were identified for cats in the initial literature search. Seventeen cats and 103 dogs with ARDS were represented across these publications. For the initial literature search in large animals, there were 83 equid, five camelid, 158 pig, 714 sheep and goat, and 270 cattle publications identified. Additionally, 1084 publications were found across all large animals that addressed interstitial lung disease. Five adult equids and 136 foals with ARDS were represented across these publications. The updated ARDSVet definitions incorporate criteria for risk factors, origin and timing of pulmonary edema, and impaired oxygenation, with severity stratified by oxygenation and definitions for both intubated and nonintubated animals. CONCLUSIONS: The evidence review and consensus process resulted in updated definitions that can be used to improve the recognition of veterinary ARDS as well as facilitate and standardize future research, including the development of an ARDS registry and eventual treatment recommendations.

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)0.087
metaresearch head score (Gemma)0.134
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.087
Threshold uncertainty score0.462

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.134
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0260.015
Science and technology studies0.0020.005
Scholarly communication0.0080.010
Open science0.0050.011
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0020.001

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 head0.084
GPT teacher head0.386
Teacher spread0.302 · 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 designTheoretical or conceptual
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

Citations5
Published2025
Admission routes1
Has abstractyes

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