A Lateral Approach for Sinus Elevation Using PRGF Technology
Bibliographic record
Abstract
PURPOSE: A lateral approach for sinus elevation using plasma rich in growth factors (PRGF) technology is described. The long-term survival of dental implants installed following a two-stage procedure after sinus elevation using this procedure is reported, using implant loss as the outcome variable. MATERIALS AND METHODS: A retrospective cohort study design was used. Eighteen patients received 43 implants (BTI implants, Biotechnology Institute, Vitoria, Spain) with sinus floor elevation. All patients presented a residual bone height of class D (1-3 mm). Implants were installed using a low-speed drilling procedure (50 rpm) without irrigation. Finally, the histological and histomorphometric evaluation of eight samples from PRGF grafted sinus involved in the study was carried out 5-6 months posttreatment. RESULTS: The overall survival rate of dental implants was 100%. The mean follow-up period for all implants was 33 +/- 7 months ranging from 24 to 44 months. In addition, the histomorphometrical evaluation of the samples evidenced a 25.24 +/- 4.62% of vital newly formed bone, 50.31 +/- 15.56% of soft connective tissue, and the remaining 24.46 +/- 12.79% of bovine anorganic bone. CONCLUSIONS: Based on these results, this new approach for sinus elevation and implant installation using PRGF technology can be considered safe, simple, effective, and predictable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".