Assessing three-dimensional soft tissue changes and the prediction of hard tissue changes after orthognathic surgery with a novel digital workflow
Bibliographic record
Abstract
To investigate the application of three-dimensional hard and soft tissue virtual surgical planning in orthognathic surgery using a novel digital workflow, we prospectively included twenty-one consecutively treated patients from two private oral surgery practices. Soft tissue facial scans were acquired using the Artec Space Spider, and intra-oral scans were obtained at one month before (T0), and at two (T1) and six months (T2) post-surgery. Cone-beam computed tomography (CBCT) scans were collected at T0 and T1. Serial three-dimensional soft and hard tissue changes were assessed by superimposing the scans in Geomagic Control X. Achieved hard tissue changes were compared to pre-surgical predictions. Differences in soft and hard tissue changes between patients treated with fixed appliances versus Invisalign® were also analyzed. The Artec Space Spider proved to be a reliable component of a novel digital workflow for virtual surgical planning, demonstrating repeatability and reproducibility. Clinically significant soft tissue relapse was observed in both the maxillary and mandibular regions between T1 and T2. Predicted surgical movements for hard tissue landmarks showed high accuracy, and soft-to-hard tissue change ratios at T1 aligned with two-dimensional data reported in the literature. No significant differences in soft or hard tissue changes were found between fixed appliances and Invisalign®. These findings provide valuable insights for enhancing surgical planning and improving clinical outcomes for both clinicians and patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".