Influence of zirconia implant surface topography on first bone implant contact within a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Although 5-year clinical data exist for different zirconia implants, no analysis has yet been performed focusing on how the surface topography of the implant affects clinical parameters. PURPOSE: To analyze the influence of zirconia implant topography on first bone implant contact (fBIC). MATERIALS AND METHODS: In a prospective two-center cohort investigation 63 zirconia implants were evaluated at implant placement, prosthetic delivery, 1, 3, and 5 years. The distance (DIB) between implant shoulder and fBIC was measured at each time point in periapical radiographs at mesial and distal sites. Two-way ANOVA/Bonferroni was used to analyze the effects of time and center (α < 0.05). RESULTS: Between the centers, the mean DIB varied significantly at implant placement (Freiburg [FR]: 1.4 ± 0.6 mm; Zurich [ZH]: 0.8 ± 0.5 mm). Thereafter, no statistically significant difference in DIB was observed, neither between centers nor between time points (prosthetic delivery: FR: 1.9 ± 0.6 mm, ZH: 1.7 ± 0.8 mm; 1 year: FR: 1.8 ± 0.6 mm, ZH: 1.6 ± 0.8 mm; 3 years: FR: 1.9 ± 0.8 mm, ZH: 1.7 ± 0.8 mm; 5 years: FR: 1.9 ± 0.8 mm, ZH 1.8 ± 0.6 mm). The overall mean DIB at prosthetic delivery to 5 years of both centers (1.8 ± 0.7 mm) is located within the transition zone between the smooth neck and the moderately rough intraosseous part (1.6-2.0 mm from the implant shoulder). However, individual DIB values are ranging from 0.1 to 4.2 mm overlapping the transition zone. CONCLUSIONS: The standard deviation of the DIB indicates that the fBIC establishes on moderately rough and smooth surfaces. Consequently, soft tissue adapts to both topographies as well.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".