Evaluation of a Jugular Venipuncture Alpaca Model to Teach the Technique of Blood Sampling in Adult Alpacas
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".