A Retrospective Evaluation of Treatments with Implant-Supported Maxillary Overdentures
Bibliographic record
Abstract
BACKGROUND: Considerably lower success rates have been presented for implant-supported overdentures in the maxilla compared with the mandible. PURPOSE: The aim of the study was to report the outcome of implant-supported maxillary overdentures from one clinic. METHODS: All patients treated with implant-supported maxillary overdentures in the Department of Prosthetic Dentistry, Central Hospital, Skövde, Sweden, between 1993 and 2002 were identified from patient charts and included in the study. All patients had a rigid cast gold alloy bar designed with ball attachments retaining an overdenture. RESULTS: Twenty-seven subjects were included, of whom 13 were originally planned for overdenture treatment (group 1) and the other 14 for a fixed prosthesis (group 2). The mean observation period was 5.7 years for subjects in group 1 and 5.5 years for those in group 2. One hundred forty-five implants were placed, and the majority of the failures were diagnosed as early ones and were found in group 2. The cumulative implant survival rate after 5 years was 77% in group 1 and 46% in group 2. The probability of having implant failure was almost three times higher among subjects in group 2 compared with subjects in group 1. Most technical and biologic complications were related to the retention system. CONCLUSION: Maxillary implant-supported overdentures show a high implant failure rate, but fewer implant failures occurred for patients originally planned for overdenture treatment.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".