Bibliographic record
Abstract
Maxillofacial correction of dentofacial deformities by means of orthognathic surgery is a common procedure nowadays. However, even if maxillary and mandibular osteotomies can greatly enhance facial aesthetic and harmony, some regions of the facial skeleton remain unchanged following conventional orthognathic surgery. Of these areas, the malar prominence and the mandibular angles warrant specific considerations. Indeed, surgical correction of malar and mandibular angles hypoplasia is challenging, and the ideal procedure or material for definitive augmentation is not yet established. With recent CAD-CAM technology advances, patient specific implant (PSI) based on mirroring algorithms have proven they to be a precise, safe and reliable option for the management of post-traumatic unilateral defect of the face. Based on that experience, PSI are now introduced in aesthetic augmentation of the facial skeleton. However, bilateral cases are much more demanding for the clinician, from a planning perspective. The complete workflow, form data-acquisition to 3D virtual treatment planning and manufacturing, will be discussed, highlighting the potential pitfalls of this rather new technology. Martin Gaboury obtained his Doctor of Dental Medicine degree in 2007 at Laval University (Quebec) and then completed his residency in Oral and Maxillofacial Surgery at l’ Hospital de l’Enfant-Jesus, affiliated with Laval University (Quebec), in 2013. He is board certified in his specialty in Canada (FRCD(C)). He obtained his Master’s degree the same year, with an award-wining thesis project focusing on orthognathic surgery. In 2015, he completed a one year clinical Fellowship in Maxillofacial and Facial Plastic Surgery in Bruges, Belgium. He is a reviewer for the International Journal of Oral and Maxillofacial Surgery and is co-author of three chapters of Prof. Gwen R J Swennen’s new book, “3D Virtual Treatment Planning of Orthognathic Surgery”.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".