Ferritin, when corrected for inflammation, is associated with increased malaria incidence in rural Zambian children (392.6)
Bibliographic record
Abstract
Poor iron status, assessed by low serum ferritin (SF), may protect against malaria. We evaluated the association of SF, and SF corrected for inflammation and malaria status (SFcorr), with microscopy‐confirmed incident malaria in a cohort of 954 4‐8 y old Zambian children participating in a 6 mo trial to improve vitamin A status (August 2012 to March 2013). Analyses were stratified by age ( < 5 y and >5 y) to account for the age‐specific risk of malaria. Baseline SFcorr was determined by proportionately reducing SF based on categories of inflammation defined by AGP > 1g/L, CRP > 5 mg/L, and by concurrent malaria status assessed by microscopy, relative to SF observed in unaffected children. SF and SFcorr levels were classified as deficient, medium, and high using cutoffs of <12 or 15 ug/L (depending on age), up to 60 ug/L (but not deficient), and >60 ug/L, respectively. Malaria‐infected children were treated at baseline. Among children <5 y, having medium or high SFcorr resulted in relative risks for incident malaria after 6 mo of 1.61 (95%CI: 1.05‐2.50) and 1.67 (95%CI: 1.06‐2.61), respectively. This pattern was not observed with uncorrected SF. Our findings demonstrate the necessity of adjusting for inflammation and concurrent malaria infection when assessing the relationship between iron status and malaria, and are consistent with evidence that iron status modifies malaria risk, particularly in young children. Grant Funding Source : Supported by HarvestPlus and Canadian International Development Agency
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".