Comparison of a Silicon Skin Pad and a Tea Towel as Models for Learning a Simple Interrupted Suture
Bibliographic record
Abstract
There has been rapid growth in the range of models available for teaching veterinary clinical skills. To promote further uptake, particularly in lower-income settings and for students to practice at home, factors to consider include cost, availability of materials and ease of construction of the model. Two models were developed to teach suturing: a silicon skin pad, and a tea towel (with a check pattern) folded and stapled to represent an incision. The models were reviewed by seven veterinarians, all of whom considered both suitable for teaching, with silicon rated as more realistic. The learning outcome of each model was compared after students trained to perform a simple interrupted suture. Thirty-two second-year veterinary students with no prior suturing experience were randomly assigned to three training groups: silicon skin pad or tea towel (both self-directed with an instruction booklet), or watching a video. Following training, all students undertook an Objective Structured Clinical Examination (OSCE), placing a simple interrupted suture in piglet cadaver skin. The OSCE pass rates of the three groups were silicon skin pad, 10/11; tea towel, 9/10; and video, 1/11. There was no significant difference between the model groups, but the model groups were significantly different from the video group ( p < .017). In conclusion, the tea towel was as effective as the silicon skin pad, but it was cheaper, simpler to make, and the materials were more readily available. In addition, both models were used effectively with an instruction booklet illustrating the value of self-directed learning to complement taught classes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".