Oral health status of patients infected with human immunodeficiency virus and related factors, Iran: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus (HIV) continues to be a major global issue. HIV-infected patients commonly experience oral health problems. This study aimed to assess oral health status of the patients and its related factors in Shiraz, Iran. METHODS: In this cross-sectional study, by using simple random sampling, 250 HIV-infected patients were selected from Shiraz Voluntary Counseling and Testing Center in 2019. Patients' decayed, missing, and filled tooth (DMFT) index, plaque index (PI), and community periodontal index (CPI) were determined. Associations between patients' characteristics and the above-mentioned indices were examined by using Pearson correlation, one-way ANOVA, chi-square, and independent sample T test. The effect of possible confounding factors was controlled by using multiple linear regression and logistic models. RESULTS: Of the 222 participants, 111 (50%) had 15 or more missing teeth and 79 (35.6%) were edentulous. Their mean (SD) DMFT and PI were 20.71 (10.74) and 1.11 (0.79), respectively. About 40% of them had healthy gingiva. DMFT (p < 0.001), PI (p < 0.001), and CPI (p = 0.002) were significantly worse in men than women. The patients' DMFT and edentulous status were significantly associated with their age (both p < 0.001) and duration of disease (p = 0.001 and p = 0.008, respectively). Unemployed patients had the worst DMFT, PI, and CPI (all p < 0.001) and the highest percentage of edentulous individuals (p = 0.003). All examined indices were significantly worse in cigarette smokers, alcoholics, addicts, and patients with a previous history of imprisonment (all p < 0.001). The odds ratio of being edentulous was 5.74 times in addicted patients than in non-addicted ones (p < 0.001). The odds increased 0.11 with every year that the patients' age increased (p < 0.001). Multiple linear regression models also showed that the addicted patients had significantly more scores in DMFT index, PI, and CPI (all p < 0.001). CONCLUSIONS: Overall, oral health status of the HIV-infected patients was unsatisfactory. Therefore, effective interventional programs are needed for prevention and early treatment of dental problems among this population, especially for more vulnerable groups such as older men with low socioeconomic status, and those with high-risk behaviors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".