One-year prosthetic outcomes with implant overdentures: a randomized clinical trial.
Bibliographic record
Abstract
PURPOSE: This randomized clinical trial examined implant overdenture (IOD) fabrication and maintenance time and costs, adjustment and repair incidence, and patient satisfaction after 1 year. MATERIALS AND METHODS: Sixty-four patients received 2 mandibular implants and an IOD with either a bar with 2 clips or 2 ball attachments for denture retention. RESULTS: Fabrication time, number of appointments, and chair time for adjustments were similar for the 2 denture designs. The most common adjustments for both types were to the IOD contours. Ball-attachment dentures required about 8 times longer for repairs than bar-clip prostheses. Approximately 84% of patients with ball-attachment dentures needed at least 1 repair, versus 20% of those with a bar-clip mechanism. The most common repairs were replacement of the cap spring or cap for the ball-attachment IOD and replacement of a lost or loose clip for bar-clip dentures. DISCUSSION: Patients were equally and highly satisfied with the improvements in function, comfort, and appearance with both types of IOD compared to their original conventional dentures. CONCLUSIONS: Given equivalent levels of patient satisfaction with either method of retention and a much higher repair rate for the ball attachment, it is suggested that a bar-clip design be used rather than the particular ball attachment utilized in this study.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".