Nanoporous TiO2 Thin Film on Titanium Oral Implants for Enhanced Human Soft Tissue Adhesion: A Light and Electron Microscopy Study
Bibliographic record
Abstract
BACKGROUND: Previous experimental studies have demonstrated direct soft tissue attachment for nanoporous titanium dioxide (TiO(2) ) thin film on implants, while implants without TiO(2) thin film have not shown this capability. PURPOSE: The aims were to evaluate and compare TiO(2) surface-modified experimental microimplants with unmodified microimplants with respect to tissue interaction of the human oral mucosa evaluated by light microscopy on ground sections and semithin sections and transmission electron microscopy on ultrathin sections, and to characterize the inflammatory response and the level of the marginal bone resorption. MATERIALS AND METHODS: The study was a single-center, randomized, comparative, clinical investigation with intrasubject comparison of implants with and without TiO(2) thin film in 15 patients. RESULTS: Two comparator microimplants showed mild erythema and expulsion of fluids. The surrounding tissues around all test implants were clinically healthy. The oral mucosa in contact with the abutment part of the microimplant was 72% for the test implants and 48% for the comparator implants, a statistically significant difference (p =.0268). No statistically significant difference was found in other histological variables. The marginal bone loss in 14 weeks was 0.5 mm for the stable test (n = 11) and 1.7 mm for the stable comparator implants (n = 9; p = .0248). CONCLUSIONS: The nanoporous TiO(2) surface modification has potential clinical benefits because of increased adherence of soft tissue and possible reduced bone resorption.
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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 | 0.000 |
| 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) | 0.001 | 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".