Nongrafted Sinus Floor Elevation with a Space‐Maintaining Titanium Mesh: Case‐Series Study on Four Patients
Bibliographic record
Abstract
PURPOSE: Numerous materials and techniques have been introduced to augment the maxillary sinus floor for future dental implant placement. Schneiderian membrane tenting above simultaneously placed implants proved to be a successful technique. The present study investigated the use of a titanium micromesh for lateral-window sinus floor elevation without bone grafting. MATERIAL AND METHODS: Four patients indicated for two-stage sinus lifting were included. Through a lateral window, a titanium micromesh was tailored and placed into the sinus to maintain the elevated membrane in place. Immediate and 6-month postoperative cone beam computed tomography (CBCT) was performed to measure the gained bone height. During implant placement, bone core biopsies were retrieved for histomorphometry. RESULTS: The average residual ridge height among the eight sinuses was 3.6 mm ± 1.6 mm. Six months postoperatively, it reached 9.63 mm ± 1.47 mm. Histomorphometry revealed that the average bone volume of the native bone was 30.3% ± 9.1%, while that of the newly formed bone was 55.3% ± 11.4%. CONCLUSION: Within the limitations of this study due to the small sample size, the use of the titanium micromesh as a space-maintaining device after schneiderian membrane elevation is a reliable technique to elevate the floor of the sinus without grafting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".