Lamb Head as a Training Model for Septoplasty and Rhinoplasty
Bibliographic record
Abstract
Septoplasty and rhinoplasty are difficult operations to learn and teach. Many modalities have been proposed to make the teaching process of these operations easier. In this study, it was investigated if lamb heads were good training models to teach septoplasty and rhinoplasty to trainees or experienced surgeons. In the first part of the study, 21 lamb heads were dissected according to a dissection protocol and several anatomical distances were measured to compare them with human cadavers. In the second part, eight lamb heads were dissected and different preservation rhinoplasty techniques were practiced. The study on 21 lamb heads used showed that the lateral crura were 17.8 × 11.6, the average interdomal distance was 8.1 mm, and the average domal width was 3.7 mm. The average length of the upper lateral cartilages was 31.1 mm laterally and 21.2 medially. The average length of the nasal bones was 63.9 mm, and the width was 16 mm. In the second part of the study, 8 lamb heads were used to experience where high-strip techniques were used in 5 and the Cottle technique in 3. This study revealed that lamb heads should be considered as an excellent training model for septoplasty and rhinoplasty. Its very low cost, ease of availability, and close similarity to the human cadavers can be counted as the main advantages. This study also proved that it was not only a tool for beginners, but also a very helpful tool for experienced surgeons to try new methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".