Maintenance Costs, Time, and Efforts Following Implant Therapy With Fixed Restorations Over an Observation Period of 10 Years: A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
OBJECTIVES: To assess the costs and efforts of maintenance therapy following implant treatment with fixed restoration over an observation period of 10 years. MATERIAL AND METHODS: This randomized controlled clinical trial included 64 patients who were randomly assigned to receive one of two implant systems (AST or STM) and fixed restoration. Patients were included in a regular maintenance program and were examined at loading, 1, 3, 5, 8, and 10 years. Outcome measures included technical and biological complications, time, efforts, and costs to resolve them. RESULTS: A total of 97 implants were placed in 64 patients (AST: 54, STM: 43). Patient recall rates at 5 and 10 years were 89% and 67%. In general, technical complications were resolved within one to two appointments (mean = 1.5), and biological complications required a mean of 1.3 appointments. The overall regular maintenance time for the period of 10 years amounted to 77 min per year. Technical complications occurred in 39.5% of the patients, with screw-loosening being the most common one (43.4% of all complications). The most time-consuming technical complication was abutment fracture (94 min ± 68), followed by screw fracture (84 min ± 38). The prevalence of peri-implant mucositis on the patient level was 30.2%, and it was 9.3% for peri-implantitis. The average annual maintenance costs amounted to 9% of the initial cost of the implant treatment over the period of 10 years. CONCLUSIONS: Additional regular maintenance costs and costs due to the treatment of potential complications have to be taken into consideration when placing dental implants. The majority of technical complications could be resolved within one appointment, whereas the time needed to treat biological complications varied between one and three appointments for peri-implantitis.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".