Complications During Maxillary Sinus Augmentation Associated with Interfering Septa: A New Classification of Septa
Bibliographic record
Abstract
Purpose: A new classification of maxillary sinus interfering septa based on its orientation is presented along with its relationship to the prevalence and severity of sinus membrane perforations. Additionally, the impact of membrane perforation on post-operative complications and marginal bone loss during the first year of loading is evaluated. Materials & Methods: Retrospective chart review of 79 consecutive sinus lift procedures with lateral window technique and 107 implants. Preoperative Cone Beam Computed Tomography (CBCT) images were evaluated for the incidence and the direction of maxillary septa. Chart notes were examined for the incidence of membrane perforation and postoperative complications. Measurements of mesial and distal marginal bone levels and average bone resorption adjacent to each implant were calculated in intraoral radiographs taken at implant placement and during follow up appointments. Results: Interfering septa were identified in 48.1 percent of sinuses. 71.1 percent of them had the septum oriented in a buccal-lingual direction (Class I). The overall incidence of membrane perforation was 22.8 percent, and the presence of an interfering septum on CBCT scan was found to be significantly associated with the occurrence of a sinus membrane perforation (P<0.001). The mean implant marginal bone loss for sinuses, which did not experience a membrane perforation, was 0.6±0.8mm, compared with 0.9 ± 0.9 mm for the sinuses that did experience a perforation (P = 0.325). Conclusion: Septa should be identified, classified and managed with a meticulous attention to technical details. A classification based on the septal orientation is proposed since the orientation of the septa can complicate the surgical procedure and requires modification of the surgical technique.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".