Location of implant‐retained fixed dentures affects oral health‐related quality of life
Bibliographic record
Abstract
Abstract Background The effects of the locations of dental implants on treatment outcomes, as evaluated by oral health‐related quality of life (OHRQoL) assessment, remain controversial. Purpose To investigate the association between the locations of dental implants and changes in OHRQoL. Materials and methods Sixty‐eight subjects received implant treatment in the anterior or posterior region and completed the Oral Health Impact Profile (OHIP) questionnaire before and after treatment. Change in OHIP summary scores and the 4 dimension scores were calculated to evaluate the effects of implant treatment on OHRQoL. Results The mean Oro‐facial Appearance score for the anterior group was significantly higher than that for the posterior group (10.4 ± 5.1 and 7.2 ± 3.8, respectively; P ＝ .005; Effect size = 0.63) at baseline. All questionnaire scores were significantly improved following implant treatment in both groups, and no significant group differences were observed at follow‐up. Regression analysis revealed a significant association between the locations of most anterior implants and changes in the Oro‐facial Appearance score (adjusted R2 = 0.073; P = .015). Conclusion Our results suggest that the locations of dental implants influence OHRQoL impairments and improvements after treatment. This information might be useful in clinical decision‐making.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".