Vertical ridge augmentation with Ti‐reinforced dense polytetrafluoroethylene (d‐PTFE) membranes or Ti‐meshes and collagen membranes: 3‐year results of a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: The present study aimed to evaluate hard and soft tissue parameters around implants placed in augmented posterior mandible, comparing Ti-reinforced d-PTFE membranes with Ti-meshes covered with collagen membranes, after 3 years of follow-up. MATERIALS AND METHODS: Forty eligible patients were randomly assigned to group A (Ti-reinforced d-PTFE membrane) or group B (mesh covered with collagen membrane) for vertical ridge augmentation (VRA) and simultaneous implants. Implants were evaluated using specific peri-implant parameters for bone and soft tissues: probing pocket depth (PPD), modified plaque index (mPI), bleeding on probing (BoP), modified gingival index (mGI), thickness of keratinized tissue (tKT), width of keratinized tissue (wKT), fornix depth (FD), peri-implant bone level (PBL), interproximal bone peaks (IBP), marginal bone loss (MBL), interproximal bone loss (IBL). RESULTS: A total of 28 patients with 79 implants were evaluated after 3 years of follow-up. The mean value of MBL was 0.70 mm (group A = 0.73 mm; group B = 0.71 mm), while mean IBL was 0.54 mm (group A = 0.64 mm; group B = 0.40 mm). The treatment with meshes resulted not inferior to PTFE and their clinical results appeared similar. A strong correlation between PBL and IBP was confirmed. Both study groups showed an increase of tKT and wKT values. CONCLUSION: In the posterior mandible, VRA using both techniques provides stable PBLs up to 3 years. A correct soft tissue management and a strict professional oral hygiene protocol play a crucial role on peri-implant health over time.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".