Long‐term evaluation of quality of life and satisfaction between implant bar overdentures and conventional complete dentures: A 23 years retrospective study
Bibliographic record
Abstract
BACKGROUND: Dental implants have been used in edentulous jaws to improve the retention and stability of complete dentures. Attachment to the implants improves stability and function of the prostheses and increases patient satisfaction. PURPOSE: The aim of this study was to evaluate quality of life and satisfaction between patients with implant overdentures and complete dentures for more than 20 years. METHODS: Forty patients with overdentures and 40 patients with conventional complete dentures were included in this study. Both groups are carriers of their prosthesis more than 20 years. All patients completed an OHIP-14 and perception and satisfaction questionnaire related their implant prothesis. RESULTS: Follow-up mean in patients with overdentures were 23.27 ± 1.87 years and 23.20 ± 3.91 years for conventional prosthesis group. A worse quality of life was shown in the group of patients with conventional dentures in the 7 dimensions and in the total value, with statistically significant differences in 6 dimensions and in the total value (P ≤ .05). Patients with implants overdenture were more satisfied than patients with conventional dentures, with statistically significant differences (P < .001). CONCLUSIONS: Implant overdentures on cobalt chrome and gold bars offer an excellent long-term solution for edentulism compared with conventional denture.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".