Histological and clinical outcomes of lateral sinus floor elevation with simultaneous removal of a maxillary sinus pseudocyst
Bibliographic record
Abstract
BACKGROUND: Maxillary sinus pathologies are a potential risk for failure of implant and bone augmentation. Management of lateral sinus floor elevation in the presence of a pseudocyst remains controversial, and reports on histological outcomes of endo-sinus bone augmentation with maxillary cysts are scarce. PURPOSE: To present a modified surgical technique for removal of maxillary pseudocyst with simultaneous sinus floor elevation, and to evaluate clinical and histological outcomes of the bone grafting. MATERIALS AND METHODS: Patients with a radiographic dome-shaped opacity in the posterior maxillary sinus were included to receive lateral sinus floor elevation with simultaneous pseudocyst removal. Bone core specimens harvested from the lateral aspect of the augmentation sites were histomorphometrically analyzed. Data were recorded and evaluated in terms of survival rates and complications. RESULTS: A total of 15 patients were included who underwent 17 maxillary sinus augmentation surgeries. Implant survival rate was 97.0%. Bone biopsy specimens were obtained at 6 months after surgery. Histomorphometric analysis revealed that mean percentages of mineralized bone, bone substitute, and nonmineralized tissue were 24.9% ± 18.1%, 14.4% ± 12.5%, and 60.1% ± 12.44%, respectively. No recurrence of the pseudocyst was detected on radiographic examination. CONCLUSIONS: The described technique could be successfully applied in clinical practice to perform sinus augmentation in the presence of pseudocysts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".