Live porcine model for surgical training in tracheostomy and open‐airway surgery
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate the validity of a live porcine model for surgical training in tracheostomy and open-airway surgery. STUDY DESIGN: Prospective observational study. METHODS: Eleven expert otolaryngologists-head and neck surgeons rated a live porcine model's realism/anatomical accuracy (face validity) and perceived effectiveness as a training tool (content validity) for tracheostomy and laryngotracheoplasty using anterior costal cartilage and thyroid ala cartilage grafts using a 53-item post-trial questionnaire with a five-point Likert scale. RESULTS: Experts rated the face validity of the live porcine model a median (interquartile range [IQR]) of 4/5 (4-5) and the content validity a median (IQR) of 5/5 (4-5) for each surgical procedure. Overall, 91% strongly agreed or agreed that the simulator would increase trainee competency for tracheostomy and laryngotracheoplasty using costal cartilage graft, and 82% strongly agreed or agreed that it would increase trainee competency for laryngotracheoplasty using thyroid ala cartilage graft. CONCLUSIONS: The live porcine model has high face and content validity as a training tool for tracheostomy and laryngotracheoplasty using costal cartilage and thyroid ala cartilage grafts. This training model can help surgical trainees practice these complex, low-frequency procedures. LEVEL OF EVIDENCE: NA Laryngoscope, 130: 2063-2068, 2020.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.009 | 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".