Fixed Implant‐Supported Prostheses in Elderly Patients: A 5‐Year Retrospective Study of 133 Edentulous Patients Older than 79 Years
Bibliographic record
Abstract
BACKGROUND: An increasing number of elderly patients are treated with implants, but results for the elderly patient in terms of implant success and adaptation to implant prostheses are contradictory. OBJECTIVE: To retrospectively study the 5-year clinical and radiologic performances of fixed implant-supported prostheses placed in edentulous elderly patients and to compare those results with the results of using similar prostheses in a control group of younger patients. MATERIALS AND METHODS: The study group comprised 133 edentulous patients who were 80 or more years of age and who were consecutively treated with fixed implant-supported prostheses between January 1986 and August 1998. Altogether 761 Brånemark System implants (Nobel Biocare AB, Göteborg, Sweden) were placed in 139 edentulous jaws. The control group comprised 115 edentulous patients who were younger than 80 years and who were treated consecutively from March 1996 to November 1997 with similar prostheses. In this group 670 implants were placed in 118 edentulous jaws. Information was collected from all postinsertion visits, including the fifth annual checkup, and changes of marginal bone levels were analyzed from intraoral radiographs. RESULTS: The 5-year cumulative survival rate (CSR) for implants in the maxilla was 93.0% in the study group and 92.6% in the control group; the corresponding CSRs for implants in the mandible were 99.5% and 99.7%. The most common complications for patients in the study group were soft tissue inflammation (mucositis) and cheek and lip biting (p < .05) whereas resin veneer fractures were the most common complications for the control group. Overall 5-year marginal bone loss for the study group was 0.7 mm (standard deviation [SD], 0.45) in the upper jaw and 0.6 mm (SD, 0.50) in the lower jaw. Differences in bone levels and bone loss between the two groups did not reach significant levels (p > .05). CONCLUSIONS: Implant treatment in the elderly patients showed treatment results comparable to those observed in younger age groups. However, indications of more problems with adaptation could be observed and were reflected in more postinsertion problems. Cleaning problems and associated soft tissue inflammation (mucositis) as well as tongue, lip, and cheek biting were significantly more often observed among the elderly patients (p < .05).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".