Role of thin gingival phenotype and inadequate keratinized mucosa width (<2 mm) as risk indicators for peri‐implantitis and peri‐implant mucositis
Bibliographic record
Abstract
BACKGROUND: There is growing evidence on the impact of thin gingival phenotype (TnP) and inadequate keratinized mucosa width (KMW <2 mm) around dental implants on peri-implant health. This study investigated the role of TnP and inadequate KMW (<2 mm) as risk indicators for peri-implantitis and mucositis and on dental patient-reported outcomes. METHODS: Sixty-three patients with 193 implants (mean follow-up of 6.9 ± 3.7 years) were given a clinical and radiographic examination and a questionnaire to assess patient awareness of food impaction and pain/discomfort. Chi-squared tests and regression analysis for clustered data were used to compare outcomes. RESULTS: Implants with TnP had a statistically higher prevalence of peri-implantitis (27.1% versus 11.3%; PR, 3.32; 95% confidence interval (CI), 1.64-6.72; P = 0.001) peri-implant mucositis (42.7% versus 33%; PR, 1.8; 95% CI, 1.12-2.9; P = 0.016) and pain/discomfort during oral hygiene (25% versus 5%; PR, 3.7; 95% CI, 1.06-12.96; P = 0.044) than thick phenotype. Implants with inadequate KMW had a statistically higher prevalence of peri-implantitis (24.1% versus 17%; PR, 1.87; 95% CI, 1.07-3.25; P = 0.027) and peri-implant mucositis (46.6% versus 34.1%; PR, 1.53; 95% CI, 1-2.33; P = 0.05) and pain/discomfort during oral hygiene (28% versus 10%; PR, 2.37; 95% CI, 1.1-5.1; P = 0.027) than the adequate KMW. TnP was strongly associated with inadequate KMW (PR = 3.18; 95% CI, 1.69-6.04; P <0.001). CONCLUSION: TnP and inadequate KMW (<2 mm) may be significant risk indicators for peri-implant disease and pain/discomfort during brushing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".