Implants with an Oxidized Surface Placed Predominately in Soft Bone Quality and Subjected to Immediate Occlusal Loading: Results from an 11‐Year Clinical Follow‐Up
Bibliographic record
Abstract
PURPOSE: The purpose of this clinical follow-up was to document the 11-year outcome of implants with a moderately rough oxidized surface subjected to immediate occlusal loading. MATERIALS AND METHODS: Twenty-six of 38 patients enrolled in a 5-year prospective study were available for this follow-up analysis, with 33 restorations supported by 66 slightly tapered implants (Brånemark System MkIV, Nobel Biocare, Gothenburg, Sweden). The majority of implants were placed in posterior regions (88%) and into soft bone (76%). Parameters included cumulative survival rate (CSR), radiographic marginal bone level, bleeding on probing (BOP), intrasulcular counts of perio-pathogenic markers (DNA probes), and total bacterial load (TBL). RESULTS: The CSR was 97.1% at 11.2 years mean follow-up. Mean marginal bone remodeling was 0.47 mm (SD 1.09, n = 65) from 1 year postplacement to 11-year follow-up. BOP was absent at most sites (63.6%). No statistically significant differences in TBL or perio-pathogenic marker species were observed at implants and teeth. CONCLUSION: The results of the present follow-up show high long-term survival, stable marginal bone levels, and soft tissue outcomes of oxidized surface implants placed predominately in posterior regions and soft bone. The quantity and quality of intrasulcular microbiota were comparable at implants and teeth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".