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Record W4394740626

The impact of skin preparation method on electrocardiogram quality in horses.

2024· article· en· W4394740626 on OpenAlexaff
Persephone McCrae, Hannah Spong, Amin Mahnam, Yana Bashura, Wendy Pearson

Bibliographic record

VenuePubMed · 2024
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsElectrodeSIGNAL (programming language)Biomedical engineeringMaterials scienceMedicineChemistryComputer science
DOInot available

Abstract

fetched live from OpenAlex

Objective: Several skin preparation techniques are used in electrocardiogram (ECG) monitoring of horses. The objective of this study was to determine which methods produce the greatest signal quality using textile electrodes and standard silver/silver chloride (Ag/AgCl) electrodes. Animals and samples: Electrocardiogram data were collected using textile and Ag/AgCl electrodes simultaneously for 4 skin preparation techniques in 6 horses. Procedure: The effects of skin preparation (cleansing with isopropyl alcohol, with or without shaving the hair) and the effects of the application of a conductive gel were assessed using metrics of signal quality. Results: Shaving and cleansing with alcohol had no effect on signal quality for either electrode type. The Ag/AgCl electrodes contain a solid gel, and the application of additional gel did not affect signal quality. Data quality was significantly improved when gel was applied to textile electrodes. Furthermore, there was no difference in signal quality between electrode types when gel was used. Conclusion and clinical relevance: This study suggests that skin preparation by cleansing and/or shaving does not have a significant effect on equine ECG signal quality. When gel is used, textile electrodes are a practical alternative for Ag/AgCl electrodes, as they produce ECG recordings of the same quality.

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.132
GPT teacher head0.496
Teacher spread0.365 · 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 designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

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