Mapping three-dimensional digital model to surgical site in facial surgery
Bibliographic record
Abstract
ABSTRACT Reconstructive surgery in the facial and oral sites requires high levels of precision. Intraoperative guidance can enhance surgical precision with three-dimensional (3D) image model. Here, case report was our endeavor of creating a 3D digital image model to guide plastic procedure is performed on the soft tissue of a patient's cheek. 3D facial structure was taken preoperatively by scanning the contours of the patient's head. The defect on patient's left cheek due to an aneurysm was identified and virtually corrected by mirroring image from the healthy right side of the cheek. Once the 3D virtual model was created, we displayed the 3D model onto the surgical site during the operation to guide surgical procedure. Digital technology is developing rapidly and is unavoidable to merge with surgical care. Clinical judgment and intraoperative performance will be improved by our efforts of integrating digital technology into the operating room.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".