Masticatory ability improves after maxillary implant overdenture treatment: A randomized controlled trial with 1‐year follow‐up
Bibliographic record
Abstract
BACKGROUND: The effect of maxillary implant overdentures on masticatory ability in edentulous patients with complaints regarding their conventional maxillary dentures is unknown. PURPOSE: To assess the change in objective masticatory ability (mixing ability index, MAI), patient reported masticatory ability (questionnaire), and patient satisfaction (GSS) after maxillary implant overdenture treatment with either solitary attachments or bars. MATERIALS AND METHODS: Two groups randomly received four-implant maxillary overdentures on either solitary attachments (group I, n = 25) or bars (group II, n = 25). The MAI, questionnaire, and GSS were scored before (T0) and 12 months (T12) after treatment. RESULTS: After treatment, both groups had significantly better MAI outcomes, better questionnaire scores and better GSS. Post-treatment questionnaire scores and GSS were significantly better for group II. Before treatment a strong, positive correlation between the MAI and the questionnaire was found for all participants who had had full conventional dentures combined (group I, n = 17; group II, n = 3). CONCLUSION: Mixing ability was the same for all the participants treated with maxillary implant overdentures on either solitary attachments or bars. Patient reported masticatory ability and satisfaction was better for participants treated with maxillary implant overdentures on bars. There was a correlation between MAI and patient reported masticatory ability in participants with full conventional dentures.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".