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Record W2266082334 · doi:10.3138/jvme.0415-067r1

Assessing the Effectiveness of a Cadaveric Teaching Model for Performing Arthrocentesis with Veterinary Students

2016· article· en· W2266082334 on OpenAlexvenueno aff
Matthew D. Johnson, Linda S. Behar‐Horenstein, Melissa A. MacIver, Y. T. Su

Bibliographic record

VenueJournal of Veterinary Medical Education · 2016
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArthrocentesisCadaveric spasmMedicineCadaverVeterinary medicineSurgeryPathologyAlternative medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to determine if a recently developed cadaveric canine model was an effective tool for teaching arthrocentesis to fourth-year veterinary students. Arthrocentesis is an important diagnostic tool and technical skill that can be difficult to teach in the clinical setting. Eighteen fourth-year veterinary students participated in a within-subjects experiment that evaluated their ability to successfully perform arthrocentesis in the canine model and in an unmodified control cadaver. Students completed an online survey about the experience. Ability to perform the procedure was assessed by monitoring the number of attempts and redirects required to enter the joint and by recording any volume recovered from the arthrocentesis. In both phases of the study, the participants were able to aspirate a measurable volume of fluid from the joints of the model. Participants recorded an increase in confidence with arthrocentesis after using the model in the first phase of the study and unanimously supported inclusion of the exercise in future teaching situations.

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)0.007
metaresearch head score (Gemma)0.003
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 score0.508
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.407
GPT teacher head0.586
Teacher spread0.179 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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