Determining Favorable Maxillary Implant Locations Using Three-Dimensional Simulation Software and Computed Tomography Data
Bibliographic record
Abstract
PURPOSE: Success rates for maxillary implant treatment are lower than for mandibular treatment because of the presence of poorer bone quality or quantity in the maxilla. The purpose of this study was to determine favorable implant positions in the maxilla using implant simulation software and clinical anatomical morphology together with bone quality data obtained by computed tomography (CT). MATERIALS AND METHODS: A convenience research sample of 10 edentulous subjects was recruited, and research information from right and left edentulous sites was obtained from each subject. The height, width, angulation, and Hounsfield unit value of the maxillary alveolar bone were measured using CT data obtained from the subjects. RESULTS: Bone height in the incisor area was significantly greater than in the molar area, and bone width in the incisor area was significantly narrower than in the molar area. The average bone quality in the maxillary molar area was significantly higher when compared with the premolar and incisor areas. The angle between the occlusal plane and the bisector of the alveolar bone in the incisor area was reduced when compared with the molar area. CONCLUSION: The premolar region appears to be the most favorable area in the maxillary arch for implant placement with regard to bone height, width, angulation, and quality.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".