Teaching facial fracture repair: A novel method of surgical skills training using three-dimensional biomodels
Bibliographic record
Abstract
Background The facial fracture biomodel is a three-dimensional physical prototype of an actual facial fracture. The biomodel can be used as a novel teaching tool to facilitate technical skills training in fracture reduction and fixation, but more importantly, can help develop diagnostic and management competence. Objective To introduce the ‘facial fracture biomodel’ as a teaching aid, and to provide preliminary evidence of its effectiveness in teaching residents the principles of panfacial fracture repair. Method Computer three-dimensional image processing and rapid prototyping were used to generate an accurate physical model of a panfacial fracture, mounted in a silicon ‘soft tissue’ base. Senior plastic surgery residents in their third, fourth and fifth years of training across Canada were invited to participate in a workshop using this biomodel to simulate panfacial fracture repair. A short didactic presentation outlining the ‘patient's’ clinical and radiological findings, and key principles of fracture repair, was given by a consultant plastic surgeon before the exercise. The residents completed a pre- and postbiomodel questionnaire soliciting information regarding background, diagnosis and management, and feedback. Result A total of 29 residents completed both pre- and postbiomodel questionnaires. Statistically significant results were found in the following areas: diagnosis of all fracture patterns (P=8.2×10 −7 [t test]), choice of incisions for adequate exposure (P=0.04 [t test]) and identifying sequence of repair (P=0.019 [x 2 test]). Subjective evaluation of workshop effectiveness revealed a statistically significant increase in ‘comfort level’ only among third year trainees. Overall, positive feedback was reported among all participants. Conclusions Biomodelling is a promising ancillary teaching aid that can assist in teaching residents technical skills, as well as how to assess and plan surgical repair.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".