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Record W2907031904

Effect of Wearing a Telemetry Jacket on Behavioral and Physiologic Parameters of Dogs in the Open-Field Test.

2017· article· en· W2907031904 on OpenAlexaboutno aff
Richard E. Fish, Paul M.D. Foster, Margaret E. Gruen, Barbara L. Sherman, Davidc C Dorman

Bibliographic record

VenuePubMed · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTelemetryVESTOpen fieldHeart rateAnxietyMedicineAudiologyAnesthesiaPsychologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Safety pharmacology studies in dogs often integrate behavioral assessments made using video recording with physiologicmeasurements collected by telemetry. However, whether merely wearing the telemetry vest affects canine behavior and otherparameters has not been evaluated. This pilot study assessed the effect of a telemetry vest on behavioral and physiologic responses to an environmental stressor, the sounds of a thunderstorm, in Labrador retrievers. Dogs were assigned to one of 2 experimental groups (Vest and No-Vest, n = 8 dogs per group) by using a matched pairs design, with a previously determined,sound-associated anxiety score as the blocking variable. Dogs were individually retested with the same standardized soundstimulus (thunderstorm) in an open-field arena, and their behavioral responses were video recorded. Video analysis of locomotor activity and anxiety-related behavior and manual determination of heart rate and body temperature were performed;results were compared between groups. Vest wearing did not affect total locomotor activity or rectal body temperature butsignificantly decreased heart rate by 8% and overall mean anxiety score by 34% during open-field test sessions. Our results suggest that the use of telemetry vests in dogs influences the measurement of physiologic parameters and behaviors that areassessed in safety pharmacology studies.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.046
GPT teacher head0.374
Teacher spread0.327 · 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 designObservational
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

Citations8
Published2017
Admission routes1
Has abstractyes

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