The role of keratinized mucosa width as a risk factor for peri‐implant disease: A systematic review, meta‐analysis, and trial sequential analysis
Bibliographic record
Abstract
BACKGROUND: Studies have examined the benefit of having keratinized peri-implant mucosa width with mixed results. PURPOSE: This study examines whether the lack of a prespecified (2 mm) amount of keratinized mucosa width (KMW) is a risk factor for peri-implant diseases. METHODS: A systematic electronic and manual search of randomized or nonrandomized controlled or noncontrolled clinical trials was conducted. Qualitative review, quantitative meta-analysis, and trial sequence analysis (TSA) of implants inserted at sites with <2 mm or ≥2 mm of KMW were analyzed to compare all the predetermined outcome variables. The level of evidence concerning the role of KMW in peri-implant health was evaluated via the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system guide. RESULTS: Nine studies were included in the qualitative analysis and four in the meta-analysis and TSA. No significant inter-group difference (p > 0.05) and a low power of evidence were found for probing depth, soft-tissue recession, and marginal bone loss. A significant difference favoring ≥2 mm KMW had a lower mean plaque index (MD = 0.37, 95% CI: [0.16, 0.58], p = 0.002) (3 studies, 430 implants, low-quality evidence). GRADE system showed very low and low quality of evidence for all other outcome measures. CONCLUSION: Based on the available studies, the impact of amount of KMW (either <2 mm or ≥ 2 mm) as a risk factor for developing peri-implant disease remains low. Future control studies with proper sample size and longer follow-up are needed to further validate current findings.
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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 | 0.034 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".