A new technique for peri‐implant recession treatment: Partially epithelialized connective tissue grafts. Description of the technique and preliminary results of a case series
Bibliographic record
Abstract
BACKGROUND: Data on implant recession coverage (RC) are very scarce. PURPOSE: To present a new surgical approach and preliminary results for the treatment of peri-implant soft tissue recession via partially epithelialized connective tissue grafts (PECTGs). MATERIALS AND METHODS: We harvested PECTGs from the palate using a double-blade scalpel. All donor sites were sutured and covered with a stent. Dissection lines were placed minimally coronal to the mucogingival border. The recipient areas were prepared epiperiostally. All PECTGs were sutured with the keratinized mucosa (KM) portion toward the local KM tissue and were subsequently widely covered by the local mucosal tissue layer. RESULTS: Fifteen patients with 22 implants were available for follow-up. The recession depth at baseline was 2.4 ± 1.1 mm (median: 2.5). After a mean observational period of 5 years, we found a mean recession value of 0.4 ± 0.5 mm (median: 0). We found a mean increase in the peri-implant KM width of 2.2 ± 1.1 mm (median: 1.5). In all cases, progression of the recession had stopped. None of the grafts was lost. The mean RC was 2 ± 0.9 mm (median: 1.5 mm) [88 ± 20% (median: 100)]. Complete RC was found in 64% of the implants. The results have remained stable for up to 13 years. CONCLUSION: Soft tissue recession around dental implants may successfully be treated using the PECTG technique.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".