Radiographic Evaluation of Maxillary Sinus Lateral Wall and Posterior Superior Alveolar Artery Anatomy: A Cone‐Beam Computed Tomographic Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of the current study is to assess the thickness of the maxillary sinus lateral wall in dentate and edentulous patients using cone beam computed tomography (CBCT). This study also provides information about the diameter, prevalence, and course of the posterior superior alveolar artery (PSAA), and its relation to the maxillary sinus floor. MATERIALS AND METHODS: Four hundred and thirty CBCT scans of the maxillofacial complex (860 maxillary sinuses) were reviewed. Measurements of the lateral wall of the maxillary sinus and PSAA were performed on the CBCT images. RESULTS: Statistical analysis showed that dental status (edentulous, non-edentulous) of the patients had no significant effect on the lateral wall thickness. The mean thickness of the lateral wall of the maxillary sinus was 1.21 ± 1.07 mm at the second molar (M2), 1.98 ± 1.87 mm at the first molar (M1), 2.02 ± 1.53 mm at the second premolar (P2) and 2.16 ± 1.25 mm at the first premolar (P1). There was statistically significant difference between the left and right sides of the maxillary sinus only at P2 (p =.043). Detection rate of the PSAA on CBCT was reported as 60.58%. The mean diameter of the artery was 1.17 mm (range 0.4-2.8 mm). There was no significant correlation between age and the size of the PSAA. The most frequent path of the PSAA was intraosseous (69.6%), followed by intrasinusal (24.3%) and superficial (6.1%). The overall mean distance of the PSAA from the floor of the maxillary sinus is 8.16 mm. CONCLUSIONS: The results from this study suggest that using CBCT prior to the surgery provides valuable diagnostic information. However, undetected intraosseous canal in CBCT does not exclude its existence. Alteration in the lateral window design and the use of piezoelectric instruments are recommended if intraoperative complications are expected.
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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 | 0.002 | 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) | 0.002 | 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".