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Record W3154679847 · doi:10.21423/aabppro20163465

Utility of an online learning module to teach cautery disbudding technique for dairy calves, including cornual nerve block application

2016· article· en· W3154679847 on OpenAlexaff
Charlotte B. Winder, S.J. LeBlanc, Derek B. Haley, K. Lissemore, Michael A. Godkin, T.F. Duffield

Bibliographic record

VenueAmerican Association of Bovine Practitioners Conference Proceedings · 2016
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsMedicineTechnicianAnimal welfareVeterinary medicineAnesthesiaEngineeringBiology

Abstract

fetched live from OpenAlex

Although disbudding or dehorning dairy heifers is necessary for the safety of humans and other cattle, it has been identified as a key animal welfare issue when done without appropriate analgesia. Three-quarters of all disbudding or dehorning is done by dairy producers or on-farm staff, while the remainder is done by a veterinarian or veterinary technician. Reported use of pain control for these procedures by dairy producers ranges from 15 to 60%. Cautery disbudding is the most commonly used method; best practices include administration of a non-steroidal anti-inflammatory drug (NSAID) as well as local anesthetic given as a cornual nerve block (CNB). While NSAID administration is uncomplicated, CNB application requires technical training, which may limit use. Teaching methods have traditionally focused on one-on-one training with a veterinarian, although online disbudding training videos exist. To our knowledge, neither method has been studied for efficacy. Our objective was to determine if an online, interactive module could teach naive participants cautery disbudding technique, including CNB, as compared to hands-on training.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.591
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.353
Teacher spread0.300 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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