MétaCan
Menu
Back to cohort
Record W2066309966 · doi:10.3138/jvme.1214-120r1

Canine Prostate Palpation Simulator as a Teaching Tool in Veterinary Education

2015· article· en· W2066309966 on OpenAlexvenueno aff
Karynn Capilé, Gabriela M.B. Campos, Rafael Stédile, Simone Tostes de Oliveira

Bibliographic record

VenueJournal of Veterinary Medical Education · 2015
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPalpationMedicineProstateRectumPhysical examinationMedical physicsPhysical therapyRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Adult dogs, especially elderly ones, are commonly affected by prostate diseases. Performing rectal palpation during physical examination in dogs is important in small animal clinical diagnosis. Prostate palpation training allows students to learn how to correctly introduce the finger into the rectum and identify the location, size, symmetry, and consistency of the prostate. Alternative methods are needed to teach this technique without using live dogs. Thus, our aim was to develop a canine prostate palpation simulator to provide students with the opportunity to learn the prostate palpation technique in dogs and to assess their opinion of this simulator as a teaching tool. The inner part of the canine mannequin contains a rotation system with three types of prostates that can be exchanged during the exam. Of the 64 participating students, 81% had never used alternative methods and 92.2% had never performed any prostatic palpation. According to the students' opinions, performing a clinical examination on a simulator allowed them to be prepared and familiarized with the palpation technique. They felt satisfied learning a practical method in a harmless way. Both the 3R (replacement, reduction, and refinement) and dog welfare principles were present in most of the students' concerns. We conclude that the simulator can help students to develop clinical skills for prostate palpation in dogs.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.095
GPT teacher head0.463
Teacher spread0.368 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Veterinary Medical EducationSame topicSimulation-Based Education in HealthcareFrench-language works237,207