Patient Evaluation after Treatment with Maxillary Implant‐Supported Overdentures
Bibliographic record
Abstract
PURPOSE: To evaluate and compare outcome among patients after implant overdenture treatment in the maxilla. MATERIALS AND METHODS: The study sample comprised two groups of patients: group 1, in which the patients were planned for overdenture treatment, and group 2, in which the patients originally were planned for a fixed prosthesis in the maxilla but had overdenture treatment owing to implant failures, resulting in an insufficient number of implants to support a fixed prosthesis. All patients treated with maxillary implant-supported overdentures in the Department of Prosthetic Dentistry, Central Hospital, Skövde, Sweden, between 1993 and 2002 received a questionnaire at their yearly follow-up visit with nine questions related to their treatment. All questions had visual analogue scale response alternatives ranging from a negative to a positive opinion. RESULTS: Nineteen patients, 10 in group 1 and 9 in group 2, completed the questionnaire, yielding a response rate of 86%. Both groups expressed a high satisfaction rate, and few regretted their choice of treatment. Patients planned for overdenture treatment (group 1) reported significantly fewer speech problems after treatment compared with those originally planned for a fixed prosthesis (group 2, p < .05). No other significant differences between the two groups were seen. CONCLUSION: Within the limitations of the present study, it can be concluded that maxillary implant overdenture treatment may be considered a viable option among patients with an insufficient number of implants for a fixed prosthesis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".