Efficacy of mechanical debridement with adjunctive probiotic therapy in the treatment of peri‐implant mucositis in cigarette‐smokers and never‐smokers
Bibliographic record
Abstract
BACKGROUND: Efficacy of mechanical debridement (MD) with adjunctive probiotic therapy (PT) in the treatment of peri-implant mucositis (PiM) in cigarette-smoking and never-smoking subjects remains uninvestigated. PURPOSE: The aim was to assess the efficacy of MD with adjunctive PT in the treatment of PiM in cigarette-smoking and never-smoking individuals. MATERIALS AND METHODS: Patients with (group I) and without PiM (group II) were included. Demographic data was collected using a questionnaire. Treatment-wise, patients in groups I and II were subdivided into: (a) Patients that underwent MD with adjunct PT (using Lactobacillus reuteri); and (b) MD alone. In all groups, probing depth (PD), plaque index (PI), and bleeding on probing (BOP) were measured at baseline and compared after 3- and 6-months. Baseline peri-implant crestal bone levels were also measured. Sample-size estimation was performed, and statistical comparisons were done using one-way analysis of variance and Bonferroni post-hoc adjustment tests. P values under .05 were deemed significant. RESULTS: Eighty individuals (group I: 40 cigarette-smokers and group II: 40 never-smokers) with PiM participated in this study. At all-time intervals, no significant difference in PD, PI, and BOP were observed in all patients in group I. At 3-months' follow-up, the differences in BOP (P < .05), and PI (P < .05) were significantly higher in group II that underwent MD + PT than MD alone. At 6-months' follow-up, there was no difference in the changes in BOP and PI among subjects that underwent MD with and without adjunct PT. CONCLUSION: On a short-term basis, MD with adjunct PT is more effectual in the treatment of PiM than MD alone in never-smokers. Cigarette-smoking compromises peri-implant soft tissue healing following MD with or without adjunct PT.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".