Ultrasonic Assessment of Mucosal Thickness around Implants: Validity, Reproducibility, and Stability of Connective Tissue Grafts at the Buccal Aspect
Bibliographic record
Abstract
PURPOSE: (1) To assess validity and reproducibility of mucosal thickness (MT) registration by means of an ultrasonic device and (2) to determine the MT stability of connective tissue grafts (CTGs) when applied at the buccal aspect of single implants demonstrating alveolar process deficiency. MATERIALS AND METHODS: For the validity assessment, four human cadaver edentulous maxillae were used to determine MT at 100 different sites. Soft tissue thickness as recorded with the ultrasonic device was compared with MT as registered with Micro-CT (UGent, Ghent, Belgium), taking the latter as gold standard. For the reproducibility assessment, 50 duplicate ultrasonic registrations were used. For the clinical part, 10 non-smoking patients with a single implant were included. All demonstrated alveolar process deficiency and had been provided with a provisional screw-retained crown at the time of inclusion. Following an intrasulcular incision at the buccal aspect, a CTG was inserted to thicken soft tissues. MT was assessed at t0 (before CTG), t1 (immediately after CTG), t2 (suture removal), t3 (permanent crown installation), and t4 (9 months after CTG). RESULTS: There was a strong correlation between ultrasonic and Micro-CT measurements (r = 0.89, p < .001). However, the former significantly underrated MT by 0.13 mm (p = .030). There was a strong correlation between duplicate ultrasonic recordings (r = 0.99, p < .001). Seven females and three males were included in the clinical study with a mean age of 52. MT significantly increased by 0.92 mm between t0 and t1 (p = .005). Between t3 and t4, there was a slight, yet significant decrease of 0.15 mm (p = .047). CONCLUSION: The ultrasonic device can be used as a non-invasive, reliable, and reproducible method to evaluate MT. Using this technology around single implants demonstrated that CTG may substantially thicken the peri-implant mucosa with acceptable stability over a 9-month period.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".