Palatal soft tissue thickness around dental implants and natural teeth in health and disease: A cross sectional study
Bibliographic record
Abstract
BACKGROUND: Previous studies focused on the influence of buccal mucosa thickness on peri-implant bone loss and inflammation, with inconclusive results. We observed substantially thicker palatal mucosal tissues at peri-implantitis sites. Therefore, we hypothesize that thick palatal peri-implant mucosa may be associated with deeper pockets and disease severity. PURPOSE: To compare the thickness of the palatal tissue between natural teeth and implants in periodontal health and disease. METHODS: Adult, non-smoker, healthy patients who visited our department for periodontal examination or treatment with restored implants in the posterior maxilla were recruited. Probing depth (PD), plaque index (PI), gingival index (GI) and radiographic measurements were recorded around implant and the contralateral tooth. Palatal tissue thickness was measured using a 30G needle that was inserted perpendicular into the mucosa at the bottom of the periodontal/peri-implant pocket and 3 mm coronally. Differences in the palatal tissue thickness between teeth and implants (in the same patient) was performed using t-test; as well as between peri-implantitis and non-peri-implantitis sites (among patients). RESULTS: Sixty patients were included. Thirty-four implants were diagnosed with peri-implantitis and 26 healthy/mucositis implants with corresponding 24 healthy/gingivitis teeth and 36 teeth with attachment loss. Mean PD was higher around implants (4.47 ± 1.57 mm) than teeth (3.61 ± 1.23 mm, p = 0.001). The thickness of implants' palatal mucosa was higher than in teeth, at the base of the pocket and 3 mm coronally (4.58 ± 1.38 mm vs. 3.01 ± 1.11, p = 0.000; 3.58 ± 2.15 vs. 1.89 ± 1.11, p = 0.000, respectively). Mean palatal tissue thickness was 4.32 ± 2.35 mm for the peri-implantitis group while only 2.61 ± 1.39 in healthy implants, 3 mm coronal to the base of the pocket (p = 0.001). Palatal thickness at peri-implantitis sites was higher (4.32 ± 2.35) compared to periodontitis sites (2.23 ± 0.93), p = 0.000. Implant sites with palatal mucosa >4 mm (n = 32) had deeper mean pockets (5.58 ± 1.98) compared with thinner (≤4 mm) sites (n = 28) (4.48 ± 1.18, p = 0.018). CONCLUSION: Thicker palatal tissue around implants is associated with deeper palatal pockets. Thick palatal tissue was found around implants diagnosed with peri-implantitis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".