The Role of Diet in Predicting: Iron Deficiency Anemia in HIV-Positive Women
Bibliographic record
Abstract
PURPOSE: The association between medical, social, and nutritional factors and iron deficiency anemia was examined in adult women who had tested positive for human immunodeficiency virus (HIV) and were living in the Greater Vancouver Area. METHODS: This was a cross-sectional observational study of 102 HIV-positive women, aged 19 or older, who were patients of one of three chosen community health clinics in Vancouver, British Columbia. Information on usual dietary intake and other nutrition-related factors was collected with a short diet survey, while medical information and laboratory data were obtained from each participant's medical chart. RESULTS: Of the predictors studied, a CD4 cell count below 200 cells/µL, a regular menstrual pattern, and African ethnicity were associated with an increased risk of iron deficiency anemia. Dietary intake was not independently associated with iron status. CONCLUSIONS: Iron deficiency anemia in HIV-positive women has multifactorial and complicated causation, but is strongly associated with poorer immune status and greater menstrual losses. Health disparities in Aboriginal and African women may lead to a higher risk for iron deficiency anemia. Routine screening and ongoing nutrition education are necessary for the prevention and management of iron deficiency anemia. Further research into factors associated with iron deficiency anemia is essential to improve prevention and management efforts.
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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 | 0.000 | 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".