Impact of Poor Oral Health on Community-Dwelling Seniors: A Scoping Review
Bibliographic record
Abstract
The aim of this scoping review was to determine health-related impacts of poor oral health among community-dwelling seniors. Using MeSH terms and keywords such as elderly, general health, geriatrics, 3 electronic databases-Medline, CINAHL, and Age Line were searched. Title and abstracts were independently screened by 3 reviewers, followed by full-texts review. A total of 131 articles met our inclusion criteria, the majority of these studies were prospective cohort (77%, n = 103), and conducted in Japan (42 %, n = 55). These studies were categorized into 16 general health outcomes, with mortality (24%, n = 34), and mental health disorders (21%, n = 30) being the most common outcomes linked with poor oral health. 90% (n = 120) of the included studies reported that poor oral health in seniors can subsequently lead to a higher risk of poor general health outcomes among this population. Improving access to oral healthcare services for elderly can help not only reduce the burden of oral diseases in this population group but also address the morbidity and mortality associated with other general health diseases and conditions caused due to poor oral health. Findings from this study can help identify shortcomings in existing oral healthcare programs for elderly and develop future programs and services to improve access and utilization of oral care services by elderly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".