Antiseptic sealant and a nanocoated implant‐abutment interface improve the results of dental implantation
Bibliographic record
Abstract
BACKGROUND: Clinical measures and implant design innovation to improve primary osseointegration and reduce the bacterial contamination of the peri-implant area are intended to reduce the incidence of late inflammatory complications in dental implantation. PURPOSE: To study the effect of nanostructured coating and antiseptic sealant on the outcomes of dental implantation. MATERIALS AND METHODS: Ninety-six individuals were clinically supervised. In the first group (Screw Ti + sealant), a special antiseptic sealant matrix was used; the same sealant was used with nanocoated implants in the second group (Nanocoat + sealant), and the conventional treatment protocol was used in the control group (Screw Ti). Patients were evaluated longitudinally during treatment and rehabilitation phases with clinical examinations, radiography, periodontal pathogen detection, and patient experience surveys. RESULTS: For patients who received a nanocoated implant and an antiseptic sealant (Nanocoat + sealant), relatively better hygienic indices were observed; there was less contamination with periodontal pathogens, bone density remained at the required level, and the overall results of treatment were better. CONCLUSIONS: Using a matrix for sealing the dental implant-abutment interface with a nanostructured surface provides reliable results regarding stable osseointegration and clinical and patient-reported outcomes of treatment success.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".