The Sheep Head Versus the Pig Head as a Training Model for Sialendoscopy
Bibliographic record
Abstract
OBJECTIVE: Sialendoscopy is a minimally invasive technique for treating inflammatory salivary gland diseases, requiring hands-on training due to its steep learning curve. While pig heads have been the standard training model for over two decades, sheep heads have recently emerged as an alternative. This study evaluates the efficiency of sheep heads for sialendoscopy training in newcomers. METHODS: Participants of the 32nd International Sialendoscopy Hands-on Course (Geneva, 2023) performed sialendoscopies on fresh pig and sheep heads. Self-assessment questionnaires were completed pre- and post-training, with procedures rated on a Likert scale. Tutors evaluated participants using standardized assessments and recorded procedural times. RESULTS: Among 52 participants from 27 countries, significant improvement in diagnostic sialendoscopy comfort was reported for both models post-training. Female participants demonstrated higher success rates than male participants in diagnostic (2.8 vs. 2.5, p = 0.03) and therapeutic (2.9 vs. 2.4, p = 0.03) procedures. More participants successfully cannulated the parotid duct on sheep heads, while submandibular gland performance was comparable across models. Self-assessments favored sheep for parotid (63%) and pigs for submandibular (79%). Overall, 53% preferred the pig model, while 47% preferred the sheep. CONCLUSION: Both models are effective for training, with sheep heads offering advantages for parotid duct procedures. Despite the pig model's broader applications, logistical, cultural, and ethical constraints make sheep heads a viable alternative for sialendoscopy training. LEVEL OF EVIDENCE: N/A.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".