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Record W2993395798 · doi:10.1111/evj.13218

Effects of a propriety oiled mixed hay feeding system on lung function, neutrophilic airway inflammation and oxidative stress in severe asthmatic horses

2019· article· en· W2993395798 on OpenAlexafffund
Alexandra Jochmans‐Lemoine, Khristine Picotte, Guy Beauchamp, Amandine Vargas, Jean‐Pierre Lavoie

Bibliographic record

VenueEquine Veterinary Journal · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversité de Montréal
FundersMitacs
KeywordsHayMedicineHay feverHorseAnimal scienceBronchoalveolar lavageLungAsthmaInternal medicineImmunologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Hay feeding is considered the main triggering factor for airway obstruction and inflammation in severe equine asthma (SEA). Finding alternate strategies allowing hay feeding while controlling clinical signs of SEA is of importance. The Nutri-Foin Système is believed to decrease inhaled dust by incorporating soybean oil to mechanically processed hay. OBJECTIVES: We compared airflow obstruction and airway inflammation in horses with SEA fed oiled hay or alfalfa pellet regimen. STUDY DESIGN: Controlled trial in asthmatic research horses. METHODS: Twelve horses in exacerbation of SEA from a research herd were studied. Horses were fed either oiled treated hay (n = 6) or alfalfa pelleted hay (n = 6) for 3 months while being stabled. Lung function, bronchoalveolar lavage fluid cytology and serum antioxidant enzyme kinetics were sequentially evaluated. RESULTS: Pelleted hay and the hay treated with the Nutri-Foin Système similarly improved lung function, airway neutrophilia and serum antioxidant enzyme kinetics over time. MAIN LIMITATIONS: The small number of horses in each group. CONCLUSIONS: We conclude from this study that Nutri-Foin Système is an appropriate alternative to pelleted hay for the control of the airway obstruction in horses with SEA.

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 categoriesMeta-epidemiology (narrow)
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 score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.299
Teacher spread0.275 · 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.

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

Citations17
Published2019
Admission routes2
Has abstractyes

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