Factors associated with the use of dental services in diabetic people
Bibliographic record
Abstract
The use of dental services prevents oral diseases where its prevalence is associated with chronic diseases such as diabetes mellitus (DM) that also has increased risks with age. Therefore, this observational, analytical and crosssectional study was conducted among 3,882 people. Data used for analysis in this research was collected from the Demographic and Family Health Survey of Peru (ENDES) from 2019 to 2021. Results from poisson regression analysis showed female gender had 1.02 times the probability of going to the dental service; likewise, people who were 91 to 100 years old had 12% more, the probability, like those with secondary education, had 8% more and those with the highest average, richest and richest wealth index had 20% more, 29% more and 29% more, respectively, the probability of going to the dental health service, as opposed to those who were very poor. The study concluded that there are several sociodemographic factors (such as being female, age progressed, natural region, those with high school, wealth index, type of residence) and personal factors (history of hypertension (HTN), physical disability) associated with a lack of access to dental services in people over 60 years old.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".