The radiological evaluation of posterior superior alveolar artery topography by using computed tomography
Bibliographic record
Abstract
BACKGROUND: Posterior superior alveolar artery (PSAA) is the most important limiting anatomic structure while lateral approach sinus surgeries. PSAA should be taken into consideration to avoid bleeding during preparation of bony window. PURPOSE: The aim of this article was to inform topography of PSAA and to evaluate measurements of this vital structure. MATERIALS AND METHODS: Three hundred and fifty-four cone-beam computed tomography (CBCT) images of PSAA from 177 patients were evaluated retrospectively. Localization of PSAA, diameter of PSAA, classification of PSAA diameter, distance between PSAA and crest, buccal bone thickness, palatal bone thickness, crest height, and crest width were recorded for each posterior tooth separately. RESULTS: The mean age of 177 patients was 54.05 ± 18.33 years. Although the most frequent localization of PSAA was intraosseous in premolar region, they were below Schneiderian membrane in molars. PSAA diameter was measured frequently less than 1 mm for all posterior teeth. Although palatal bone thickness was higher in premolar region than molars, no statistical relationship was found between tooth region and buccal bone thickness (P > 0.05). The width of residual ridge was measured both wider apically and posteriorly. Positive correlation was observed between buccal bone thickness and PSAA diameter in first molar and premolar regions (P < 0.05). CONCLUSIONS: Detailed evaluation of patients by CBCT provided us the opportunity to draw topography of PSAA and inform about overall measurements of PSAA in all posterior teeth region.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".