Multiple Infections, Nutrient Deficiencies, and Inflammation as Determinants of Anemia and Iron Status during Pregnancy: The MINDI Cohort
Bibliographic record
Abstract
= 213 Panamanian indigenous women), we investigated if hemoglobin, anemia (Hb < 110 g/L), ferritin, serum iron, serum transferrin receptor, and hepcidin were associated with (1) maternal nutritional status and supplementation practices, (2) biomarkers of inflammation, and (3) presence/absence of infections. Hierarchical generalized linear and logistic regression models and dominance analyses identified the relative importance of these predictors. Anemia (38%), which was likely underestimated due to low plasma volume (95%), was associated with lower ferritin, vitamin A, and weight-for-height, suggesting anemia of undernutrition. Inflammation was not associated with Hb or anemia; nevertheless, higher CRP was associated with increased odds of low serum iron and higher ferritin and hepcidin, indicating iron restriction due to inflammation. The length of iron supplementation did not enter models for anemia or iron indicators, but a multiple nutrient supplement was associated with higher ferritin and hepcidin. Moreover, iron supplementation was associated with higher odds of vaginal trichomoniasis but lower odds of caries and bacterial vaginosis. The complex pathogenesis of anemia and iron deficiency in MINDI settings may require other interventions beyond iron supplementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".