Cone Beam Computed Tomographic Measurement of Maxillary Central Incisors to Determine Prevalence of Facial Alveolar Bone Width ≥2 mm
Bibliographic record
Abstract
BACKGROUND: The initial thickness of maxillary bone has significant impact on the responding level of facial bone and soft tissue after extraction and immediate implant placement. A prevailing notion is that following implant placement in fresh extraction sites, at least 2 mm of facial bone is needed to prevent soft tissue recession, fenestration, and dehiscence. PURPOSE: The purpose of this study was to use cone beam computed tomography (CBCT) to measure horizontal width of facial alveolar bone overlying healthy maxillary central incisors and to determine prevalence of bone thickness ≥2 mm. MATERIALS AND METHODS: Tomographic data from 101 randomly selected patients were evaluated by two independent observers. Assessments were made of facial bone width at levels 1.0 to 10.0 mm apical to the bone crest. RESULTS: Healthy maxillary central incisors (n= 202) were measured from 101 patient scans. The percent of teeth with facial bone ≥2 mm at levels 1, 2, 3, 4, and 5 mm from the bone crest was 0, 1.5, 2.0, 3.0, and 2.5%, respectively. Overall mean thickness of the bone was 1.05 mm for right and left central incisors combined. The range of individual measurements for all levels was 0 to 5.1 mm. The occurrence of ≥ 2 mm thickness bone measurements increased with increasing depth. However, mean widths observed at levels 6 to 10 mm from the crest ranged only 1.0 to 1.3 mm because of apparent fenestration occurrence (0 mm bone) in approximately 12% of teeth. Overall, no significant differences in bone thickness were found between ethnic, gender, age, or scan groups. CONCLUSIONS: Using CBCT, occurrences of ≥2 mm maxillary facial alveolar bone were found on no more than 3% of root surfaces 1.0 to 5.0 mm apical to the bone crest in this sample of maxillary central incisors. The study evidenced prevalence of a thin facial alveolar bone (<2 mm) that may contribute to risk of facial bone fenestration, dehiscence, and soft tissue recession after immediate implant therapy.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".