Evaluating the benefit of virtual surgical planning on bony union rates in head and neck reconstructive surgery
Bibliographic record
Abstract
OBJECTIVES: Virtual surgical planning (VSP) has gained acceptance because of its benefits in obtaining adequate resection, achieving cephalometric accuracy, and reducing operative time. The aim of this study is to compare the rate of union between VSP and free-hand surgery (FHS), identify predictors of non-union and evaluate the difference in operative time. METHODS: Post-operative CT were retrospectively reviewed for 123 patients who underwent maxillary or mandibular reconstruction between 2014 and 2021 using either VSP or FHS. Each apposition was graded as complete, partial or non-union. The rate of union, risk difference and inter-rater reliability were calculated. The difference in operative time was assessed. Predictors of non-union were identified using logistic regression. RESULTS: A total of 326 appositions were graded (VSP n = 150; FHS n = 176). The rates of complete and partial union were higher with VSP than FHS (74.7% vs. 65.3%; 18% vs. 15.9%, respectively, p = 0.01). Non-union was found at a higher rate with FHS than with VSP (18.7% vs. 7.3%). The non-union risk difference was 11.4. FHS, major complications and apposition at the native bone were predictors of non-union (OR 2.9, p = 0.02; OR 3.4, p = 0.01; OR 2.5, p = 0.05, respectively). The mean surgical time was shorter with VSP than with FHS (265.3 vs. 381.5 min, p < 0.001). The inter-rater agreement was high (k = 0.85; ICC = 0.86). CONCLUSION: VSP demonstrated significantly higher bony union rates and shorter operative time. FHS, development of major complications and apposition with native bone correlated with non-union.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".