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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".