The Effect of Keratinized Mucosa Width on Peri‐Implant Outcome under Supportive Postimplant Therapy
Bibliographic record
Abstract
BACKGROUND: Long-ranging data on the influence of keratinized mucosa (KM) on peri-implant tissue status have been scarce. PURPOSE: Retrospective evaluation of peri-implant diseases and KM width in patients with versus without mucogingival surgery. MATERIALS AND METHODS: Under supportive postimplant therapy (SIT) in a private practice, 68 patients with peri-implant KM widths <1 mm were identified between 1992 and 2011 (eight dropouts). Thirty patients rejected surgery (control [C] group), and 30 patients agreed (intervention [I] group). After at least 1 year, KM width, mucositis, and peri-implant conditions were assessed. RESULTS: Sixty nonsmoking patients (n = 105 implants) were available for assessment after 12.10 ± 4.93 years. No implants were lost (survival rate: 100%). An average of 10.69 years after surgery, the I group implants showed a mean KM gain of 3.10 ± 1.43 mm (C group: 0 mm). The mucositis rates were as follows: I group: 38.98%; C group: 31.91%. Peri-implantitis was detected in two implants (1.87%) and two individuals (6.67%) in the I group. No significant differences between groups were found, except that the KM width values were significantly greater in the I group (p < 0.001). CONCLUSIONS: Low incidences of peri-implant diseases over long periods can be expected in patients attending SIT programs, independent of the absence or presence of KM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".