The relationship between accessory maxillary ostium, maxillary sinus pathologies, and sinonasal region variations
Bibliographic record
Abstract
Abstract Purposes: The aim of this study is to evaluate the prevalence of accessory maxillary ostium (AMO) and its association with age, gender, sinus variation, pathological formations, status of dentition and tooth endodontic and periodontal status using cone-beam computed tomography (CBCT).Methods : The study examined archived CBCT images of 390 patients at least 18 years old (232 females and 158 males) representing 790 regions of interest. The mean age of the female patients was 46.35 and that of the males was 48.62. All the CBCT images analyzed with sagittal, coronal and axial sections. The data were assessed using IBM SPSS 22. Significance was evaluated at the p <0.05 level. Results: The study observed that while the prevalence of primary maxillary ostium (PMO) on the right side was 94.9% and on the left side was 94.6% (for a total of 94.7%), the prevalence of accessory ostium on the right side was 33.1% and on the left was 35.4% (34.2% in total).Conclusions: There was no significant difference in AMO prevalence related to age, presence of mucosal thickness, mucus retention cysts, maxillary sinusitis, sinonasal variation, status of dentition or periodontal status of posterior maxilla. However there was a relationship between AMO and both gender and endodontic status.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".