Quantitation of Mandibular Symphysis Volume as a Source of Bone Grafting
Bibliographic record
Abstract
BACKGROUND: Autogenous intramembranous bone graft present several advantages such as minimal resorption and high concentration of bone morphogenetic proteins. A method for measuring the amount of bone that can be harvested from the symphysis area has not been reported in real patients. PURPOSE: The aim of the present study was to intrasurgically quantitate the volume of the symphysis bone graft that can be safely harvested in live patients and compare it with AutoCAD (version 16.0, Autodesk, Inc., San Rafael, CA, USA) tomographic calculations. MATERIALS AND METHODS: AutoCAD software program quantitated symphysis bone graft in 40 patients using computerized tomographies. Direct intrasurgical measurements were recorded thereafter and compared with AutoCAD data. The bone volume was measured at the recipient sites of a subgroup of 10 patients, 6 months post sinus augmentation. RESULTS: The volume of bone graft measured by AutoCAD averaged 1.4 mL (SD 0.6 mL, range: 0.5-2.7 mL). The volume of bone graft measured intrasurgically averaged 2.3 mL (SD 0.4 mL, range 1.7-2.8 mL). The statistical difference between the two measurement methods was significant. The bone volume measured at the recipient sites 6 months post sinus augmentation averaged 1.9 mL (SD 0.3 mL, range 1.3-2.6 mL) with a mean loss of 0.4 mL. CONCLUSION: AutoCAD did not overestimate the volume of bone that can be safely harvested from the mandibular symphysis. The use of the design software program may improve surgical treatment planning prior to sinus augmentation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".