Impact of keratinized mucosa on implant‐health related parameters: A 10‐year prospective re‐analysis study
Bibliographic record
Abstract
AIM: To investigate whether the lack of keratinized mucosa (KM) affects peri-implant health after 10 years of loading. MATERIALS AND METHODS: Data from 74 patients with 148 implants from two randomized controlled studies comparing different implant systems were included and analyzed. Clinical parameters including bleeding on probing (BOP), probing depth (PD), plaque index, marginal bone loss (MBL), and KM width (KMW) at buccal sites were collected at baseline (time of the final prosthesis insertion), 5-year and 10 years postloading. Multivariable logistic and linear regression models by means of a generalized estimated equation (GEE) were used to evaluate the influence of buccal KM on peri-implant clinical parameters; BOP, MBL, PD, and adjusted for implant type (one-piece or two-piece) and compliance. RESULTS: A total of 35 (24.8%) implants were healthy, 67 (47.5%) had mucositis and 39 (27.6%) were affected by peri-implantitis. In absence of buccal KM (KM = 0 mm), 75% of the implants exhibited mucositis, while in the presence of KM (KMW >0 mm) 41.2% exhibited mucositis. Regarding peri-implantitis, the corresponding percentages were 20% (KM = 0 mm) and 26.7% (KM >0 mm). Unadjusted logistic regression showed that the presence of buccal KM tended to reduce the odds of showing BOP at buccal sites (OR: 0.28 [95% CI, 0.07 to 1.09], p = 0.06). The adjusted logistic regression model revealed that having buccal KM (OR: 0.21 [95% CI, 0.05 to 0.85], p = 0.02) and using two-piece implants (OR: 0.34 [95% CI, 0.15 to 0.75], p = 0.008) significantly reduced the odds of showing BOP. Adjusted linear regression by means of GEE showed that KM and two-piece implants were associated with reduced MBL and MBL changes (p < 0.05). CONCLUSION: The lack of buccal KM appears to be linked with peri-implant parameters such as BOP and MBL, but the association is weak. The design of one-piece implants may account for their increased odds of exhibiting BOP.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".