Relationship among Schneiderian Membrane, Underwood's Septa, and the Maxillary Sinus Inferior Border
Bibliographic record
Abstract
BACKGROUND: Osseo-integrated implants are increasingly being used to restore functional dentition; however, in the posterior region, implant placement can be problematic because of inadequate bone height. In this condition, maxillary sinus floor elevation surgery has become the treatment of choice. The presence of anatomic variations within the maxillary sinus such as Underwood's septa and thin Schneiderian membrane decreases the success of the sinus floor elevation. PURPOSE: In this study, we tried to determine the relationship between the anatomic variations of the maxillary sinus: Underwood's septa, Schneiderian membrane thickness, and the cortical thickness of the inferior border of the maxillary sinus. MATERIAL AND METHODS: The left and right maxillary sinus images of 74 patients were obtained by using dental computed tomography (CT). The Schneiderian membrane and the cortical thickness of the inferior border of the maxillary sinus were measured on the coronal images of dental CT scans at the deepest portion of the sinus cavity. The presence of Underwood's septa was identified on the axial images. The correlations between these variables were assessed. RESULTS: We found that there was only a negative correlation between the Schneiderian membrane thickness and the presence of Underwood's septa (r = -0.168 p = .042). CONCLUSION: It is suggested that Underwood's septa may be the reason for the thinness of the Schneiderian membrane. However, future studies among larger groups are necessary for confirming the finding by using well-designed clinical studies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".