Virtual Surgical Planning: The Pearls and Pitfalls
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it