Influence of keratinized mucosa width on the resolution of peri‐implant mucositis: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The prevalence of peri-implant diseases, driven by biofilm accumulation and influenced by factors such as the width of keratinized mucosa (KM), underscores the need for understanding their etiology and management. PURPOSE: To evaluate the association between the KM width and the clinical resolution of peri-implant mucositis after mechanical therapy. MATERIALS AND METHODS: Patients with an implant diagnosed with peri-implant mucositis were allocated to two groups: wide band of KM (WKM ≥ 2 mm) and narrow/no band of KM (NKM < 2 mm). Data and submucosa biofilm were collected at baseline and at 8, 12, and 24 weeks after nonsurgical therapy. A Brunner-Langer model was estimated for longitudinal data to evaluate and compare changes in any clinical parameter throughout follow-up between both groups. Furthermore, the microbial profiles were evaluated by 16S rRNA gene sequencing. RESULTS: A total of 38 implants were analyzed. At 24 weeks, bleeding on probing was substantially reduced in both groups, reaching statistical significance (p < 0.001). Treatment resulted in 23.9% less effective in achieving success for NKM. As such, NKM reduced the odds of disease resolution by 80% compared to WKM. The rest of the explored clinical parameters yielded more favorable outcomes for WKM versus NKM. Neither the alpha nor the beta diversity of the microbial profiles were significantly modulated by KM. CONCLUSIONS: KM width influences the clinical resolution of peri-implant mucositis after mechanical therapy (https://clinicaltrials.gov/study/NCT04874467?cond=keratinized%20mucosa&rank=8, NCT04874467, 04/30/2021).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".