The Comparison of Porous Titanium Granule and Xenograft in the Surgical Treatment of Peri‐Implantitis: A Prospective Clinical Study
Bibliographic record
Abstract
BACKGROUND: Regarding the current approach, there is no evidence to show which treatment technique is the most accurate and useful in peri-implant defects. PURPOSE: The aim of this study is comparing the effect of porous titanium granule (PTG) with rotary titanium brush and the use of xenograft and collagen membrane in the treatment of intra-bony peri-implant defects. MATERIALS AND METHODS: Twenty-two patients, suffering peri-implantitis defects were included this study. Patients were divided into two groups: The PTG group used rotary titanium brush, PTG, and platelet rich fibrin (PRF) membrane. The XGF group used xenograft bone substitute, collagen membrane, and PRF membrane. Clinical measurements and cone beam computed tomography per region were recorded as baseline and sixth month after surgery. RESULTS: The mean CAL values were improved from 5.29 ± 1.06 to 3.59 ± 0.88 mm in PTG group, while in XGF group; these values were improved from 4.77 ± 1.05 to 3.30 ± 0.58 mm. Radiographic bone filling values displayed a statistically significant difference between of groups. In PTG groups, these radiological values increased more than the XGF group. CONCLUSIONS: PTG may be more appropriate for peri-implantitis surgery than xenograft due to inert structure and comfortable use of PTG to provide mechanical support for enlarging the surface area of the implant.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".