Implant Placement in Combination with Sinus Membrane Elevation without Biomaterials: A 1‐Year Study on 15 Patients
Bibliographic record
Abstract
BACKGROUND: Membrane elevation in combination with implant placement without biomaterials is a rather new technique proposed for sinus lifting. PURPOSE: This study assessed the clinical outcome of such technique during the first year of loading. MATERIAL AND METHODS: Fifteen patients with a mean residual bone height of 6.2 mm were consecutively recruited for sinus lifting. After opening a replaceable bone window, the membrane was dissected from the sinus walls. A total of 28 implants were placed in the residual crest and they kept the membrane lifted upwards. After window repositioning, the flap was sutured. A 6-month healing period was allowed. Patients were re-examined after 12 months of loading. RESULTS: All the implants survived at the end of the follow-up. The 5.5 mm mean bone reformation was significantly lower than the 8.2 mm mean membrane lift achieved after implant placement. Regeneration at the distal surface of the most posterior implants was significantly less than at other aspects. The height of membrane lift was not correlated with the amount of regenerated bone. CONCLUSIONS: All of the 28 implants placed in combination with sinus membrane elevation were stable during the first year of loading. No extra costs for biomaterial or morbidity for bone harvesting were necessary.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".