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Record W4321502254 · doi:10.3138/jvme-2022-0127

Evaluation of an Equine Nasogastric Intubation Model for Training Veterinary Students

2023· article· en· W4321502254 on OpenAlexvenueno aff
Alison M. Prutton, Holly A. H. Lenaghan, Sarah Baillie

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntubationMedical educationClinical PracticeVeterinary medicineNursingSurgery

Abstract

fetched live from OpenAlex

Simulation in veterinary education is an important means of providing a safe, welfare-friendly way for students to hone their skills prior to performing procedures on live animals. Students may not get many chances to practice passing a nasogastric tube and checking for reflux in live horses during clinical rotations and extramural studies. A low-cost equine nasogastric intubation model was created at the University of Surrey, allowing students to practice passing a tube and checking for reflux. Thirty-two equine veterinarians evaluated the model for realism and its potential usefulness in teaching. Veterinarians found the model to be realistic, supported its use as a teaching aid, and provided helpful feedback for possible improvements. In addition, 83 year-4 veterinary students rated their level of confidence before and after using the model for nine specific aspects of nasogastric intubation. Students showed significantly increased confidence levels in all nine aspects after using the model, and reported that they appreciated being able to practice the skill in a safe environment prior to performing it on a live horse. The results of this study suggest that both clinicians and students considered that this model has educational value, which supports its use for training veterinary students prior to clinical placements. The model provides an affordable, robust educational aid that can be used in clinical skills teaching, increases student confidence, and allows students to practice the skill repeatedly.

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)0.013
metaresearch head score (Gemma)0.007
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 score0.875
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.798
GPT teacher head0.667
Teacher spread0.131 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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