The effect of mandibular 2‐implant overdentures on oral health–related quality of life: an international multicentre study
Bibliographic record
Abstract
OBJECTIVES: To determine the difference in oral health-related quality of life (OHRQoL) in patients who received mandibular 2-implant overdentures and conventional dentures in a pragmatic international study. MATERIALS AND METHODS: In this prospective study, data were gathered from 203 edentulous patients (mean age, 68.8; SD: 10.4 years) at eight centres in North America, South America and Europe. The patients were provided with new mandibular conventional dentures or implant overdentures supported by 2 implants and ball attachments and opposed by conventional dentures. At baseline and at 6 months post-treatment, patients rated their oral health-related quality of life using the OHIP-20. RESULTS: A significantly higher proportion of the participants in the implant group in North America reported improvement in both the psychological and the handicap domains, compared to those who received conventional dentures (93% vs. 52%; P < 0.05). In South America, 100% of participants who received implant overdentures reported improvement in physical pain, compared to 66% in the conventional group (P < 0.05). Differences in mean change scores among those who expressed improvement were not significantly different between sites or treatments. CONCLUSION: Mandibular 2-implant overdentures are more likely than conventional dentures to improve OHRQL for edentulous patients. Cultural differences were also observed in the impact of implant overdentures on the different domains of the OHIP-20.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".