The influence of interfering septa on the incidence of Schneiderian membrane perforations during maxillary sinus elevation surgery: a retrospective study of 52 consecutive lateral window procedures
Bibliographic record
Abstract
Abstract Aim: Sinus lifts are a predictable method of augmenting the height of bone in maxillary posterior sextants. These procedures can be complicated by anatomical factors, such as the presence of interfering bony septa in the sinus. The objectives of this study were to investigate the incidence of interfering septa in patients undergoing sinus lifts and to see if the presence of interfering septa increased the chance of intra‐operative membrane perforation. Materials and methods: This retrospective cohort study assessed presence of interfering antral septa and their effect on Schneiderian membrane elevation in 45 patients with pneumatised sinuses undergoing sinus lifts. Chart audits and radiographic assessments were performed for 52 surgeries. The sinus lift procedure followed established guidelines. Presence of septa and occurrence of perforations were noted, and when perforations occurred, they were repaired with resorbable membranes. Results: Septa were present in 40% of cases, and were found to be ‘interfering’ septa in 28.8% of cases. Membrane perforation occurred in 11.5% of cases. There was no statistically significant association between the presence of interfering septa and membrane perforation. Conclusion: With enough experience and appropriate armamentarium and technique, an operator can overcome the presence of an interfering antral septum during a sinus lift procedure such that it does not increase the chance of perforating the Schneiderian membrane during elevation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".