A 3‐year longitudinal prospective study assessing microbial profile and clinical outcomes of single‐unit cement‐retained implant restorations: Zirconia versus titanium abutments
Bibliographic record
Abstract
PURPOSE: The aim of this study was to assess the microbiological and clinical outcomes of implant-supported restorations on zirconia or titanium abutments after 3 years in function. MATERIALS AND METHODS: Twenty two-part dental implants were placed in 20 healthy individuals in need of single-tooth replacement. Ceramic-based reconstructions were cemented in either zirconia or titanium abutments. Clinical, radiographic, and microbiological outcomes were examined at implant loading and then yearly up to 3 years post-loading. RESULTS: Cumulative survival/success implant rates were 95% after 3 years. Mean total marginal bone loss was 0.76 ± 0.21 mm for zirconia and 0.99 ± 0.41 mm for titanium, with no significant differences (P > .05). Overall, titanium and zirconia abutments presented similar values of probing depth, gingival recession, and bleeding on probing over time (P < .05). Microbial profile of implants restored with titanium or zirconia is quite similar to that found in the remaining teeth. CONCLUSIONS: Zirconia and titanium presented different microbial profile and genome counts. Clinical findings for both zirconia and titanium abutments were similar and consistent with a healthy condition, reflecting a high survival rate and low bone loss. Microbiota did not impact the clinical outcomes after 3 years of function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".