Serum Ferritin is Associated with HIV Disease Severity & Mortality among Postpartum Zimbabwean Women
Bibliographic record
Abstract
The relationship between iron status & HIV infection is complex & poorly understood. While anemia is a major complication of HIV infection, higher iron stores may be associated with disease progression. It is uncertain if increased iron stores are a cause of disease progression or a consequence. There is limited & conflicting data available from Africa, & none that examine this relationship prospectively. We examined the association between post‐partum iron status & viral load (VL), progression (maternal death by 12 months), & MTCT of HIV in 643 HIV+ Zimbabwean women. In non‐anemic women a log10 increase in SF was associated with a 2.3‐fold increase in VL (p=0.02); this association was absent in anemic women. At the same time, Hb was negatively associated with VL (p<0.01). In prospective analyses, a log10 increase in SF was associated with a 3.5‐fold increase in the risk of death by 12 months (p=0.01), but there was no association with MTCT. Controlling for AGP, a marker of inflammation, attenuated the association between SF & VL or progression, but these remained significant. These results are consistent with the hypothesis that high iron stores have adverse consequences in HIV infection, however AGP may not fully control for the effect of HIV on SF (reverse causality). If adverse consequences are real, our data suggest that they occur at SF levels > 45 ug/L, and do not include MTCT. Funding by CIDA, USAID, Nestle, and Cornell Univ
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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 | 0.000 | 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 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".