Food insecurity and low CD4 count among HIV-infected people: a systematic review and meta-analysis
Bibliographic record
Abstract
Food insecurity is defined as a limited or uncertain ability to acquire acceptable foods in socially acceptable ways, or limited or uncertain availability of nutritionally adequate and safe foods. While effective antiretroviral treatment can significantly increase CD4 counts in the majority of patients, there are certain populations who remain at relatively low CD4 count levels. Factors possibly associated with poor CD4 recovery have been extensively studied, but the association between food insecurity and low CD4 count is inconsistent in the literature. The objective is to systematically review published literature to determine the association between food insecurity and CD4 count among HIV-infected people. PubMed, Web of Science, ProQuest ABI/INFORM Complete, Ovid Medline and EMBASE Classic, plus bibliographies of relevant studies were systematically searched up to May 2015, where the earliest database coverage started from 1900. Studies that quantitatively assessed the association between food insecurity and CD4 count among HIV-infected people were eligible for inclusion. Study results were summarized using random effects model. A total of 2093 articles were identified through electronic database search and manual bibliographic search, of which 8 studies included in this meta-analysis. Food insecure people had 1.32 times greater odds of having lower CD4 counts compared to food secure people (OR = 1.32, 95% CI: 1.15-1.53) and food insecure people had on average 91 fewer CD4 cells/µl compared to their food secure counterparts (mean difference = -91.09, 95% CI: -156.16, -26.02). Food insecurity could be a potential barrier to immune recovery as measured by CD4 counts among HIV-infected people.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".