Evaluation of 0.2% delmopinol mouth rinse for prevention of peri‐implant mucositis and peri‐implantitis: A randomized controlled canine study
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effectiveness of 0.2% delmopinol mouth rinse in maintenance of peri-implant tissue health and prevention or inhibition of peri-implant mucositis and peri-implantitis. MATERIALS AND METHODS: Four weeks following tooth extraction, eight titanium dental implants, were placed in six dogs' mandibles. Three dogs were assigned to the test or placebo mouth rinse. Eight weeks following implant installation (T2) ligature was placed to induce peri-implant disease. Clinical and radiographic assessment was performed during the study period as well as micro-CT analysis and histologic assessment to evaluate Bone-Implant Contact at the end of the follow-up (T4). RESULTS: Plaque at implant sites before ligature placement (T2) was lower for the test group (0.7 ± 1.0 vs 1.5 ± 1.3, P < .05). The ratio of affected implant (PD ≧4 mm) at T2 and T4 in the test group was significantly smaller than that of the placebo group (T2, 17% vs 47%, P < .01; T4, 67% vs 83%, P < .05). The test agent also seemed to be effective in partially preventing bone loss induced by ligature placement according to the Computed Tomography and histologic analysis (test, 1.1 ± 0.8 mm; placebo, 1.5 ± 0.9 mm). CONCLUSIONS: Within the limits of this animal model study, the results of the study indicate that the 0.2% delmopinol rinse might play a role in prevention of peri-implant disease development.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".