Implants with an Oxidized Surface Placed Predominately in Soft Bone Quality and Subjected to Immediate Occlusal Loading: Results from a 7‐Year Clinical Follow‐Up
Bibliographic record
Abstract
PURPOSE: The purpose of this clinical follow-up was to document the 7-year outcome of immediately loaded implants exhibiting an oxidized surface. MATERIAL AND METHODS: Thirty-eight patients received a total of 51 implant-supported fixed prostheses. The restorations were supported by 102 implants, the majority of which were placed in posterior regions (88%) and primarily in soft bone quality (76%). Radiographic examinations were performed at prosthesis insertion, at 1- and 6-month follow-ups, and annually at the 1- through 5-year follow-up visits. Marginal peri-implant soft tissue evaluations were conducted at all these follow-ups. This report presents the results after at least 7 years of loading. RESULTS: After 7 years of prosthetic loading, the cumulative implant survival rate was 97.1% and the mean marginal bone remodeling was -1.51 mm (SD 1.00, n = 73) with significantly more initial remodeling at sites having received marginal guided bone regeneration procedures. A low rate of biological and technical complications was detected after 7 years of function. The quantification of intrasulcular plaque sampling showed no significant difference between teeth and implants. CONCLUSION: The 7-year follow-up data indicate that the introduced immediate loading protocol is a successful treatment alternative also including regions exhibiting soft bone conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".