The impact of Middle Eastern Origin, HIV, HCV, and HIV/HCV co‐infection in the development of hypovitaminosis D in adults
Bibliographic record
Abstract
BACKGROUND: A relationship between hypovitaminosis D and infection with HIV and HCV has been established in the scientific literature. Studies comparing these illnesses to other risk factors for development of hypovitaminosis D, such as being of Middle Eastern origin, have been lacking. The goals of this study were: (a) to document vitamin D levels in groups of individuals at high risk of developing its deficiency, (b) analyze the data collected to numerically determine which group had the lowest average vitamin D levels, and (c) discuss the impact of the findings and offer possible explanations. METHODS: This retrospective observational study involved reviewing medical charts and documenting recent vitamin D levels. Our subgroups were: (a) individuals infected with HIV, (b) individuals infected with HCV, (c) individuals co-infected with HIV/HCV, and (d) people of Middle Eastern origin. The gathered data was subsequently subjected to statistical analysis. RESULTS: People of Middle Eastern origin were found more likely to be vitamin D deficient as compared to those infected with HIV, HCV, or co-infected with both HIV and HCV. CONCLUSION: This suggests that genetic and environmental factors unique to otherwise healthy Middle Eastern people are more detrimental, in terms of developing hypovitaminosis D, than being chronically infected with the aforementioned illnesses.
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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 | 0.002 |
| 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 | 0.000 |
| 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".