The width of keratinized mucosa around dental implants and its influencing factors
Bibliographic record
Abstract
BACKGROUND: A few evidence is available in the literature concerning the pattern of variation in the width of keratinized mucosa (KMW) around dental implants and factors that may affect the KMW. PURPOSE: The purpose of this study is to investigate the KMW at the buccal aspect of dental implants and to analyze its influencing factors. MATERIALS AND METHODS: The current study was a retrospective study conducted on 726 patients with 1252 dental implants. The following parameters were evaluated by reviewing the medical records of each patient, including the age, gender and smoking status of each patient, the reasons of teeth loss, the position of implants, the bone augmentation procedures, and the KMW. Binary logistic regression analysis with the generalized estimating equations was utilized to analyze the factors that may affect the KMW of dental implants. RESULTS: The KMW of implants located in the maxilla was significantly higher than that of implants located in the mandible (P < .01), except for the upper and lower canines. The logistic regression analysis indicated that the risk of the implants presenting inadequate KMW (<2 mm) in the periodontitis-caused tooth loss group was 1.91 times of the non-periodontitis-caused tooth loss group. The risk of implants presenting inadequate KMW after receiving simple and complex bone augmentation procedures was 1.65 and 2.62 times of the risk of implants without bone augmentation, respectively. The longer the follow-up period, the higher the risk of implants presenting inadequate KMW will be. CONCLUSIONS: The KMW at the buccal aspect of implants is related to the position of implants. Tooth loss due to periodontitis, the bone augmentation procedures, and the process of functional period would increase the risk of implants presenting an inadequate amount of keratinized mucosa.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".