Access to Dental Services among Hypertensive Elderly in Peru: Exploring Patterns and Implications
Bibliographic record
Abstract
Background: This study was conducted to determine access to dental services in the elderly with hypertension in Peru. Methods: Observational, analytical, and cross-sectional design. Data used for analysis in this research was collected from the Demographic and Family Health Survey of Peru (ENDES) from 2019 to 2021. Results: A Poisson regression analysis was performed a weighted sample for calculating prevalence ratio (PR) with their 95% confidence intervals (95%CI). The multiple regression analysis did not find among the factors associated with the probability of using the dental health service, since neither the time less than two years of hypertension (PR=0.74, 95%CI 0.53 – 1.02); nor from 2 to 4 years (PR = 0.97, CI 95% 0.86 – 1.09); neither a time of hypertension from 5 years or more (PR = 0.94, CI 95% 0.85 – 1.03) were associated. Conclusion: The study concluded that hypertensive patient over 60 years of age, despite a previous diagnosis of hypertension or not, does not attend dental service, reflecting a lack of interest and a greater risk exposure to cardiovascular complications associated with oral health.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".