Bone and soft tissue outcomes, risk factors, and complications of implant‐supported prostheses: 5‐Years RCT with different abutment types and loading protocols
Bibliographic record
Abstract
BACKGROUND: Data on risk factors and complications after long-term implant treatment is limited. The aims were to evaluate the role of various fixation modes and to analyze complications and risks that affect long-term use of implant-supported partial fixed dental prostheses. MATERIALS AND METHODS: Fifty partially edentulous subjects received three Brånemark TiUnite™ implants. Superstructures were attached directly at implant level (IL) or via abutments: machined surface (AM) and an oxidized surface (AOX, TiUnite™). Implants were immediately loaded (test) or unloaded for 3 months (control). Examinations occurred over a 5-year period. RESULTS: Forty-four subjects were re-examined after 5 years. Cumulative survival rates in test and control groups were 93.9% and 97.0%, respectively. Marginal bone loss (MBL; Mean [SEM]) was significantly lower at superstructures connected to AM (1.61 [0.25] mm) than at sites with no abutment IL (2.14 [0.17] mm). Peri-implantitis occurred in 9.1% of subjects and in 4.0% of implants. Multiple linear regression indicated that increased probing pocket depth (PPD), periodontal disease experience, deteriorating health, and light smoking (≤10 cigarettes/day) predict greater MBL, whereas increased buccal soft tissue thickness and higher ISQ predict lower MBL. CONCLUSIONS: The results show that MBL was influenced by the connection type. A machined abutment, instead of connecting the superstructure directly at the implant level, was beneficial. The following factors influenced MBL: PPD, periodontal disease experience, deteriorating health, light smoking, buccal soft tissue thickness, and ISQ. The results on peri-implantitis underscore the need for long-term maintenance care. Further, the abutment material surface properties constitute additional target for strategies to minimize MBL.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".