The Association of Oral Health Status and socio‐economic determinants with Oral Health‐Related Quality of Life among the elderly: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine the relationship between poor Oral Health-Related Quality of Life (OHRQoL) and oral health determinants (eg being 75 years of age or greater, marital status, smoking status, denture wearing, depression, low educational level (≤8th grade), poor general health, caries history, tooth-induced pain, decayed, missing filled teeth (DMFT) scores and periodontal diseases) among the elderly. METHODS: Formal search strategies in PubMed, Scopus, Cochrane and Web of Science were performed to identify studies in English published before 1 December 2019. We assessed the impacts of the oral health determinants including being 75 years of age or greater, marital status, smoking status, denture wearing, depression, low educational level (≤8th grade), poor general health, caries history, tooth-induced pain, DMFT scores and periodontal diseases) on OHRQoL among elderly individuals. The data were analysed using Stata 12.0 software. RESULTS: In total, 19 publications met the inclusion criteria of this meta-analysis. Findings indicate a positive association between low educational level (ie ≤8th grade), marital status, depression, smoking status, denture wearing, poor general health, tooth-induced pain, periodontal diseases and poor OHRQoL among the elderly. We also observed a negative association between DMFT, being older than 75 years of age on poor OHRQoL among the elderly. CONCLUSIONS: This review identified that several oral health determinants were associated with poor OHRQoL. The efficacy of preventive measures and the economic aspects of tooth replacement approaches should be explored in the future. Developing oral healthcare plans and policies with the specific aim of improving OHRQoL among this group is essential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".