Inflammatory effects of individualized abutments bonded onto t<scp>itanium base</scp> on peri‐implant tissue health: A randomized controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: In implant prosthodontics, computer-assisted design and computer-assisted manufacturing (CAD/CAM) zirconia abutments bonded onto titanium bases are frequently used in prosthetic dentistry. Unpolymerized monomer of the bonding material or the adhesive gap itself may have a negative effect on peri-implant tissue health. However, evidence addressing this problem is not available. PURPOSE: The aim of the current trial was to study inflammatory effects of individualized abutments bonded onto titanium bases. MATERIAL AND METHOD: A total of 24 patients with one test abutment and one control abutment each participated in this prospective, double-blind, randomized controlled clinical trial. Thereby, test abutments were CAD/CAM titanium abutments bonded onto titanium abutments (Ti-Base). As control abutments individualized, one-piece CAD/CAM titanium abutments were used. At abutment installation as well as 6 and 12 months thereafter bone level changes, clinical parameters as well as Il-1β levels were assessed. RESULTS: Neither for bone level or clinical parameters nor for Il-1β levels, significant differences between test and control abutments could be found. However, in both groups Il-1β levels were significantly elevated at both the 6 and 12 months follow-up compared to baseline. CONCLUSION: Within the limits of this RCT, it can be concluded that effects on the inflammatory state of peri-implant tissues do not differ between individualized abutments bonded onto Ti-Bases and individualized one-piece abutments.
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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 | 0.004 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".