Assessment of Maxillary Sinus Septa Using Cone‐Beam Computed Tomography: Etiological Consideration
Bibliographic record
Abstract
BACKGROUND: Septum presence in the maxillary sinus complicates sinus floor elevation surgery, and so it is important that septa are accurately diagnosed on preoperative imaging. PURPOSE: Septa were observed regarding their relationship with the bony palate using cone-beam computed tomography (CT). MATERIALS AND METHODS: Thirty maxillary sinuses with dentate jaws of 15 dry skulls and a cone-beam CT unit were used. A septum was defined as a pointed bone structure, and an exostosis was defined as a rounded bone structure. The occurrence and locations of maxillary sinus septa and exostoses of more than 2 mm in height were evaluated. Also, angles between the direction of septum and median palatine suture were measured on axial images. RESULTS: Twelve septa of 11 maxillary sinuses (37%) and nine exostoses of nine maxillary sinuses (30%) were observed. Also, 42% of septa and 67% of exostoses were antero-posteriorly aligned according to the transverse palatine suture. Moreover, the mean septum angle was 57.9 degrees in the anterior maxillary sinus region, and 101.8 degrees in the transverse palatine suture region, and significant differences were noted between them. CONCLUSION: Maxillary sinus septa and exostoses could be clarified regarding their relationship with the bony palate using cone-beam CT.
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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 | 0.002 |
| 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) | 0.001 | 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".