Quantitation of Mandibular Ramus Volume as a Source of Bone Grafting
Bibliographic record
Abstract
BACKGROUND: When alveolar atrophy impairs dental implant placement, ridge augmentation using mandibular ramus graft may be considered. In live patients, however, an accurate calculation of the amount of bone that can be safely harvested from the ramus has not been reported. The use of a software program to perform these calculations can aid in preventing surgical complications. PURPOSE: The aim of the present study was to intra-surgically quantify the volume of the ramus bone graft that can be safely harvested in live patients, and compare it to presurgical computerized tomographic calculations. MATERIALS AND METHODS: The AutoCAD software program quantified ramus bone graft in 40 consecutive patients from computerized tomographies. Direct intra-surgical measurements were recorded thereafter and compared to software data (n = 10). In these 10 patients, the bone volume was also measured at the recipient sites 6 months post-sinus augmentation. RESULTS: The mandibular second and third molar areas provided the thickest cortical graft averaging 2.8 +/- 0.6 mm. The thinnest bone was immediately posterior to the third molar (1.9 +/- 0.3 mm). The volume of ramus bone graft measured by AutoCAD averaged 0.8 mL (standard deviation [SD] 0.2 mL, range: 0.4-1.2 mL). The volume of bone graft measured intra-surgically averaged 2.5 mL (SD 0.4 mL, range: 1.8-3.0 mL). The difference between the two measurement methods was significant (p < 0.001). The bone volume measured 6 months post-sinus augmentation averaged 2.2 mL (SD 0.4 mL, range: 1.6-2.8 mL) with a mean loss of 0.3 mL in volume. CONCLUSION: The mandibular second molar area provided the thickest cortical graft. A cortical plate of 2.8 mm in average at combined second and third molar areas provided 2.5 mL particulated volume. The use of a design software program can improve surgical treatment planning prior to ramus bone grafting. The AutoCAD software program did not overestimate the volume of bone that can be safely harvested from the mandibular ramus.
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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) | 0.001 | 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".