Changes in alveolar bone width around maxillary implants, as determined through cone beam computed tomography based on bony landmarks: A preliminary study
Bibliographic record
Abstract
PURPOSE: This study aimed to investigate changes in alveolar bone width around dental implants and identify the anterior nasal spine (ANS), posterior nasal spine (PNS), and floor of the nasal cavity that can be used as reference landmarks for standardizing the orientation of different cone-beam computed tomography (CBCT) scans. MATERIALS AND METHODS: We enrolled two groups that comprised 30 implants. Two CBCT scans from the same patient after implant surgery in the first group were obtained to determine differences in the relative distance and angle between the ANS and apex of the dental implant. Then we compared the second group of patients' presurgical and postsurgical CBCT images to evaluate changes in alveolar bone width after dental implant surgery by the aforementioned bony landmarks. RESULTS: In the first group, no statistically significant differences were detected in the mean distance between the ANS, PNS and implant tip in different directions. In the second group, bone width increased at 1 mm (p = 0.020) and decreased at 4 mm (p < 0.001) and 7 mm (p < 0.001) below the alveolar bone crest after implant surgery. CONCLUSIONS: Within the limitations of the present study, the ANS, PNS, and floor of the nasal cavity can be useful in standardizing the orientation of CBCT scans and alveolar bone remodeling after implant surgery varied depending on the height and direction from the alveolar bone crest based on the three landmarks.
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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 | 0.001 | 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".