Contextual and individual determinants of tooth loss in the Afro-descendant older adult populations of different countries: a scoping review.
Bibliographic record
Abstract
BACKGROUND: The Black population has poorer oral health than other racial groups; however, little is known about the mechanisms that explain this difference. OBJECTIVE: To study the association between race and tooth loss and map the evidence on factors associated with tooth loss in Black older populations. METHODS: Scoping review following the PRISMA Extension for Scoping Reviews conducted according to the recommendations of the Joanna Briggs Institute. A three-step search strategy was applied, and data were collected between April and July 2021. Searches were performed in the PubMed, Lilacs, and SciELO databases. The grey literature was searched using Google Scholar (https://www.scholar.google.com/). The reference lists of included studies were used as additional sources. Studies published in English and Portuguese of the association between tooth loss and different racial groups and the factors associated with tooth loss and tooth retention in Black older adult populations were included. RESULTS: Twenty-one of 913 original articles published between 1995 and 2020 were included. Of these, 75% were research articles, 15% were reports, and 10% dissertations. Eighty per cent reported cross-sectional and 20% longitudinal data. African ancestry was associated with increased odds of tooth loss in older adult populations. Periodontal disease, female sex, and advanced age were the exposures most frequently associated with tooth loss. CONCLUSION: Race, educational level, advanced age, and oral diseases such as periodontitis are associated with increased tooth loss in Afro-descendant older populations.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".