Food insecurity and low CD4 count among HIV-infected people: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it