Osseotite Implant: 3‐Year Prospective Multicenter Evaluation
Bibliographic record
Abstract
PURPOSE: This prospective multicenter study evaluates the cumulative success rate of the Osseotite implant after 3 years of prosthetic loading. MATERIALS AND METHODS: A total of 413 Osseotite implants (Implant Innovations) were placed in 142 patients (completely or partially edentulous) in five dental offices exclusively devoted to implants. The average age of the patients was 58.3 years. Of the 413 implants, 191 were placed in the maxilla and 222 in the mandible; 271 (65.6%) were posterior implants and 142 (34.4%) were anterior implants. Clinical and radiographic evaluations were made after completion of the prosthetic restoration, after 6 months of loading, at 1 year, and at 3 years. RESULTS: A cumulative success rate of 95.3% was obtained after 3 years of prosthetic loading. The success rate was similar in both arches: 95.1% in the maxilla and 96.8% in the mandible. Early failures (before prosthetic loading) were greater (n = 12) than late failures (n = 2). After 3 years of prosthetic loading, the marginal bone level of 385 (93.2%) implants were evaluated radiographically. Bone level was at the first thread for 91.4% of the implants. A slightly increased loss was observed around 26 implants (6.7%). Including survival implants, the cumulative implant success rate after 3 years was 96%. A success rate of 98.4% was obtained with 187 short implants (8, 5 and 10 mm) reported in this multicenter evaluation. CONCLUSION: This multicenter evaluation demonstrates excellent predictability for Osseotite implants.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".