The influence of titanium base abutments on peri‐implant soft tissue inflammatory parameters and marginal bone loss: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Some techniques in implant dentistry have been suggested that may potentially alter peri-implant soft and hard tissue parameters. PURPOSE: To evaluate the peri-implant soft tissue inflammatory parameters and crestal bone loss around titanium base abutments. MATERIALS AND METHODS: Fifty two implants were placed in 21 patients and restored by single crowns. Subjects were randomly allocated into two groups: cement-retained abutment (n = 24) and titanium base (n = 28). Peri-implant probing depth, and mesial and distal marginal bone loss (MBL) were evaluated at implant loading (T1), 6 and 12 months (T2 and T3, respectively). Peri-implant bleeding-on-probing was evaluated at T2 and T3. Two-way repeated measures analysis of variance, Tukey test, Man Whitney, and Pearson correlation were performed for statistical analysis at P < .05. RESULTS: The mean difference of peri-implant MBL from implant installation to 12 months in function was 1.15 ± 0.82 mm for the cement-retained group, and 1.23 ± 0.79 mm for the titanium base group. No statistically significant difference was found between the two groups for clinical and radiographic peri-implant evaluation. CONCLUSIONS: Titanium base abutments present no negative effect on peri-implant soft tissue and MBL. When used to support single crowns, both approaches performed likewise regarding clinical and radiographic parameters.
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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 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".