Long‐term Survival of Straumann Dental Implants with<scp>TPS</scp>Surfaces: A Retrospective Study with a Follow‐up of 12 to 23 Years
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the long-term dental implant survival rates of Straumann dental implants in a university hospital environment over 12 to 23 years. MATERIALS AND METHODS: A total of 388 Straumann dental implants with titanium-sprayed surfaces (TPS) were inserted in 92 patients between 1988 and 1999 in the Department of Oral and Maxillofacial Surgery of the University Hospital Schleswig-Holstein in Kiel, and they were reevaluated with standardized clinical and radiological exams. Kaplan-Meier analyses were performed for individual factors. Cox proportional hazard regression analysis was used to detect the factors influencing long-term implant failure. RESULTS: The long-term implant survival rate was 88.03% after an observation time of 12.2 to 23.5 years. Cox regression revealed statistically significant influences of the International Team for Implantology (ITI) implantation type (p = .00354) and tobacco smoking (p = .01264) on implant failure. A proportion 82.8% of the patients with implant losses had a medical history of periodontitis. Peri-implantitis was diagnosed in 9.7% of the remaining implants in the long-term survey. CONCLUSIONS: This study emphasized the long-term rehabilitation capabilities of Straumann dental implants in complex cases. The survival rates after several years constitute important information for patients, as well as for clinicians, in deciding about different concepts of tooth replacement. Patient-related and technical factors - determined before implant placement - could help to predict the risk of implant loss.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".