Retrospective cohort study of 4591 Straumann implants in private practice setting, with up to 10‐year follow‐up. Part 1: multivariate survival analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this retrospective, noninterventional, open cohort study is to report on the long-term survival of dental implants, in private practice representing the daily realities of implant treatment. The data are analyzed to discern statistical relationships between explanatory variables and implant failure. MATERIALS AND METHODS: A total of 4591 Straumann implants were placed in 2060 patients between 1999 and 2012. Patients were evaluated after 2-3 months, 1, 3, 5, and 7 years and, in some cases, up to 10 years. The cumulative survival rate (CSR) was calculated according to the life table method and illustrated with Kaplan-Meier survival curves. Univariate analysis was performed to investigate the association between study variables and time to implant-failure. Variables with P -value < 0.15 were further selected for a multivariate analysis. Statistical methods which take into account the fact that some patients have more than one implant (therefore, dependency between implants within mouth) had been applied. RESULTS: At the implant level, the cumulative survival rates at 3, 5, and 7 years were 99.3%, 99.0%, and 98.4%, respectively, and at the patient level, they were 98.6%, 97.7%, and 95.9%, respectively. After adjustment to possible confounders, the multivariate analysis identified a relationship between the following risk indicators for implant failure: implant location, length and design, timing of implantation, bone grafting procedures and gender. Tissue-Level implants (n = 3863) had a very high survival rate of 99% at 3 years, which was maintained over the entire study period. Bone-Level implants (n = 600) were as predictable with a survival rate of 99% up to 3 years, while Tapered Effect implants (n = 128) demonstrated a lower survival rate of 95% at 5 years. Short 6-mm implants in the mandibular posterior sites had a high survival rate of 100%, while in maxillary posterior positions a survival rate of only 87% was achieved. Patient factors such as smoking, autoimmune disease, and penicillin allergy were tending to associate with higher failure rates. CONCLUSION: High long-term survival rates were observed for a large cohort of Straumann implants. Tissue- and Bone-Level implants had higher survival rates than Tapered Effect implants, and although short implants faired well in the mandibular posterior sites, they faired less well in the maxillary posterior sites. The study represents private practice insight into large-scale, long-term implant results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".