Synthetic Simulator for Surgical Training in Tracheostomy and Open Airway Surgery
Bibliographic record
Abstract
OBJECTIVE(S): To create and validate a synthetic simulator for teaching tracheostomy and laryngotracheal reconstruction (LTR) using anterior costal cartilage and thyroid ala cartilage grafts. METHODS: A late adolescent/adult neck and airway simulator was constructed based on CT scans from a cadaver and a live patient. Images were segmented to create three-dimensional printed molds from which anatomical parts were casted. To evaluate the simulator, expert otolaryngologists - head and neck surgeons performed tracheostomy and LTR using anterior costal cartilage and thyroid ala cartilage grafts on a live anesthetized porcine model (gold standard) followed by the synthetic simulator. They evaluated each model for face validity (realism and anatomical accuracy) and content validity (perceived effectiveness as a training tool) using a five-point Likert scale. For each expert, differences for each item on each simulator were compared using Wilcoxon Signed-Rank tests with Sidak correction. RESULTS: Nine expert faculty surgeons completed the study. Experts rated face and content validity of the synthetic simulator an overall median of 4 and 5, respectively. There was no difference in scores between the synthetic model and the live porcine model for any of the steps of any of the surgical procedures. CONCLUSION: The synthetic simulator created for this study has high face and content validity for tracheostomy and LTR with anterior costal cartilage and thyroid ala cartilage grafts and was not found to be different than the live porcine model for these procedures. LEVEL OF EVIDENCE: 5 Laryngoscope, 131:E2378-E2386, 2021.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".