Guided maxillary sinus floor elevation using deproteinized bovine bone versus graftless Schneiderian membrane elevation with simultaneous implant placement: Randomized clinical trial
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to evaluate the analytical difference between the use of xenograft (control group) and graftless tenting (test group) technique after sinus lift procedure with simultaneous implant placement. MATERIALS AND METHODS: Seventeen patients and 20 sinuses where operated for sinus lift procedures using lateral window approach with simultaneous implant placement. Deproteinized bovine bone (Xenograft) was used as a filling material in control group while nongrafted sinus lifting was performed in the test group. Multislice CT was obtained preoperatively and CBCT were obtained immediately postoperative and 6 months after operation. Osstell readings were taken at the time of implant placement and implant exposure (6 months) RESULTS: Mean bone height gain in the xenograft group was 8.59 ± 0.74 while that of the tenting group was 4.85 ± 0.5 and it was statistically significant (P < .05). Mean bone density values in the xenograft group was 375.59 ± 49.38 while that of the tenting group was 269.08 ± 16.27 and it was statistically significant (P < .05). Mean ISQ values for the xenograft group was 78.3 ± 5.08 while that of the tenting group was 74 ± 3.19 and it was statistically significant (P < .05). CONCLUSIONS: Within the limitation of this study, sinus lift procedures with simultaneous implant placement using xenograft as a filling material or graftless technique are considered reliable procedures, however, the use of xenograft provide better results in all aspects regarding (bone height gain, bone density, and implant stability).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".