Teaching Ear Reconstruction Using an Alloplastic Carving Model
Bibliographic record
Abstract
BACKGROUND: Ear reconstruction is challenging surgery, often with poor outcomes. Our purpose was to develop a surgical training model for auricular reconstruction. METHODS: Silicone costal cartilage models were incorporated in a workshop-based instructional program. Trainees were randomly divided. Workshop group (WG) participated in an interactive session, carving frameworks under supervision. Nonworkshop group (NWG) did not participate. Standard Nagata templates were used. Two further frameworks were created, first with supervision then without. Groups were combined after the first carving because of frustration in the NWG. Assessment was completed by 3 microtia surgeons from 2 different centers, blinded to framework origin. Frameworks were rated out of 10 using Likert and visual analog scales. Results were examined using SPSS (version 14), with t test, ANOVA, and Bonferroni post hoc analyses. RESULTS: Cartilaginous frameworks from the WG scored better for the first carving (WG 5.5 vs NWG 4.4), the NWG improved for the second carving (WG 6.6 vs NWG 6.5), and both groups scored lower with the third unsupervised carving (WG 5.9 vs NWG 5.6). Combined scores after 3 frameworks were not statistically significantly different between original groups. A statistically significant improvement was demonstrated for all carvers between sessions 1 and 2 (P ≤ 0.09), between sessions 1 and 3 (P ≤ 0.05), but not between sessions 2 and 3, thus suggesting the necessity of in vitro practice until high scores are achieved and maintained without supervision before embarking on in vivo carvings. Quality of carvings was not related to level of training. CONCLUSIONS: An appropriate and applicable surgical training model and training method can aid in attaining skills necessary for successful auricular reconstruction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".