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Record W2972796194 · doi:10.1136/vr.104684

Conditioning equine athletes on water treadmills significantly improves peak oxygen consumption

2019· article· en· W2972796194 on OpenAlexaff
Persephone Greco‐Otto, Stephanie L. Bond, R. H. SIDES, Warwick M. Bayly, Renaud Léguillette

Bibliographic record

VenueVeterinary Record · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineHeart rateAnimal scienceTreadmillVentilation (architecture)ConditioningAnesthesiaTidal volumeBlood lactatePhysical therapyInternal medicineMathematicsRespiratory systemBlood pressureBiology

Abstract

fetched live from OpenAlex

Equine water treadmills (WT) were initially designed for rehabilitation of musculoskeletal injuries, but are also commonly used for conditioning sport horses, however the effects are not well documented. The purpose of this study was to test the effect of an 18‐day WT conditioning programme on peak oxygen consumption (V̇O 2 peak). Nine unfit Thoroughbreds were used in a randomised controlled trial. Six horses worked daily for 18 days in stifle‐height water (WT group), while 3 control horses worked without water (dry treadmill group (DT)). Preconditioning and postconditioning maximal exercise racetrack tests (800 m) were performed using a portable ergospirometry system. Measured outcomes were V̇O 2 , tidal volume, minute ventilation, breathing frequency, heart rate, blood lactate and instantaneous and average speed. The workload as assessed by V̇O 2 was 21.7 per cent of preconditioning V̇O 2 peak values for WT horses. V̇O 2 peak on the racetrack increased by 16.1 per cent from preconditioning to postconditioning in the WT horses (P=0.03), but did not change in the DT horses. Therefore, exercising horses in high water heights may improve conditioning.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.021

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 head0.100
GPT teacher head0.361
Teacher spread0.261 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2019
Admission routes1
Has abstractyes

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