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Record W2619168290 · doi:10.3138/jvme.1115-188r

Evaluation of a Jugular Venipuncture Alpaca Model to Teach the Technique of Blood Sampling in Adult Alpacas

2017· article· en· W2619168290 on OpenAlexvenueno aff
Marjolaine Rousseau, Guy Beauchamp, Sylvain Nichols

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVenipunctureMedicineBlood samplingPhysical therapyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

The effectiveness of teaching aids in veterinary medical education is not often assessed rigorously. The objective in the present study was to evaluate the effectiveness of a commercially available jugular venipuncture alpaca model as a complementary tool to teach veterinary students how to perform venipuncture in adult alpacas. We hypothesized that practicing on the model would allow veterinary students to draw blood in alpacas more rapidly with fewer attempts than students without previous practice on the model. Thirty-six third-year veterinary students were enrolled and randomly allocated to the model (group M; n=18) or the control group (group C; n=18). The venipuncture technique was taught to all students on day 0. Students in group M practiced on the model on day 2. On day 5, an evaluator blinded to group allocation evaluated the students' venipuncture skills during a practical examination using live alpacas. Success was defined as the aspiration of a 6-ml sample of blood. Measured outcomes included number of attempts required to achieve success (success score), total procedural time, and overall qualitative score. Success scores, total procedural time, and overall scores did not differ between groups. Use of restless alpacas reduced performance. The jugular venipuncture alpaca model failed to improve jugular venipuncture skills in this student population. Lack of movement represents a significant weakness of this training model.

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)0.009
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.544
GPT teacher head0.606
Teacher spread0.063 · 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.

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

Citations8
Published2017
Admission routes1
Has abstractyes

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