Effect of implant therapy on oral health‐related quality of life (OHIP‐49), health status (SF‐36), and satisfaction of patients with several agenetic teeth: Prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Effect of fixed prosthodontics on patients with several agenetic teeth is not well understood. PURPOSE: To assess the effect of implant-based fixed prosthodontics on oral health-related quality of life (OHRQoL), general health status, and satisfaction regarding dental appearance, ability to chew and speech in patients with several agenetic teeth. MATERIALS AND METHODS: For this prospective cohort study, all patients (≥18 years) with several agenetic teeth who were scheduled for treatment with fixed dental implants between September 2013 and July 2015 at our department were approached. Participants received a set of questionnaires before and 1 year after implant placement to assess OHRQoL (OHIP-NL49), general health status (SF-36), and satisfaction regarding dental appearance, ability to chew and speech. RESULTS: About 25 out of 31 eligible patients (10 male, 15 female; median age: 20 [19;23] years; agenetic teeth: 7 [5;10]) were willing to participate. Pre- and post-treatment OHIP-NL49 sum-scores were 38 [28;56] and 17 [7;29], respectively (P < .001). Scores of all OHIP-NL49 subdomains decreased tool, representing an improved OHRQoL (P < .05) as well as that satisfaction regarding dental appearance, ability to chew and speech increased (P < .001). General health status did not change with implant treatment (P > .05). CONCLUSIONS: Treatment with implant-based fixed prosthodontics improves OHRQoL and satisfaction with dental appearance, ability to chew and speech, while not affecting general health status.
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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) | 0.001 | 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 | 0.001 |
| 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".