Virtual Surgical Planning: The Pearls and Pitfalls
Bibliographic record
Abstract
OBJECTIVE: Over the past few years, virtual surgical planning (VSP) has evolved into a useful tool for the craniofacial surgeon. Virtual planning and computer-aided design and manufacturing (CAD/CAM) may assist in orthognathic, cranio-orbital, traumatic, and microsurgery of the craniofacial skeleton. Despite its increasing popularity, little emphasis has been placed on the learning curve. METHODS: A retrospective analysis of consecutive virtual surgeries was done from July 2012 to October 2016 at the University of Montreal Teaching Hospitals. Orthognathic surgeries and free vascularized bone flap surgeries were included in the analysis. RESULTS: Fifty-four virtual surgeries were done in the time period analyzed. Forty-six orthognathic surgeries and 8 free bone transfers were done. An analysis of errors was done. Eighty-five percentage of the orthognathic virtual plans were adhered to completely, 4% of the plans were abandoned, and 11% were partially adhered to. Seventy-five percentage of the virtual surgeries for free tissue transfers were adhered to, whereas 25% were partially adhered to. The reasons for abandoning the plans were (1) poor communication between surgeon and engineer, (2) poor appreciation for condyle placement on preoperative scans, (3) soft-tissue impedance to bony movement, (4) rapid tumor progression, (5) poor preoperative assessment of anatomy. CONCLUSION: Virtual surgical planning is a useful tool for craniofacial surgery but has inherent issues that the surgeon must be aware of. With time and experience, these surgical plans can be used as powerful adjuvants to good clinical judgement.
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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 | 0.035 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".