A prospective study of the clinical outcomes of peri‐implant tissues in patients treated for peri‐implant mucositis and followed up for 54 months
Bibliographic record
Abstract
BACKGROUND: Peri-implant mucositis is very common and, when left untreated, can progress to the more serious condition of peri-implantitis. Therefore, early diagnosis, adequate treatment and, in particular, adherence to a peri-implant supportive therapy are extremely important for the management of peri-implant mucositis. PURPOSE: Characterize the clinical conditions of peri-implant tissues in patients diagnosed with peri-implant mucositis after undergoing peri-implant supportive therapy for 54 months. MATERIALS AND METHODS: Thirty-eight patients (131 dental implants) who received peri-implant supportive therapy, associated with oral hygiene instructions, were assessed at baseline and at 54 months for visible plaque and gingival bleeding indexes, probing depth and bleeding on probing. Data were statistically analyzed using the Chi-square test and relative risk assessment using a significance level set at 5%. RESULTS: None of the independent variables evaluated (age, gender, smoking, type of prosthesis, time using the prosthesis, keratinized mucosa, phenotype peri-implant, classification of visible plaque index and classification of gingival bleeding index) presented significant associations with "worsening" or "improvement" of clinical parameters. CONCLUSION: The implementation of peri-implant support therapy was not sufficient for the resolution of peri-implant mucositis, although reductions in clinical parameters with respect to baseline were observed and maintained during follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".