Iron deficiency prevalence among pregnant women in Cambodia varies widely by trimester, inflammation adjustments, and across different ferritin thresholds
Bibliographic record
Abstract
Iron deficiency (ID) prevalence has been consistently reported as low among non-pregnant women in Cambodia, but less is known about iron status during pregnancy. Assessing iron status during pregnancy is critical, as deficiency can increase the risk of adverse pregnancy outcomes. We assessed anemia, ID, and inflammation prevalence in a cohort of pregnant women in Cambodia. Venous blood from 90 pregnant women (12-32 weeks' gestation) was collected before the start of a 2016 trial conducted in Prey Veng province. Gestational age was recorded on the same day as blood collection. Hemoglobin was measured on a hematology autoanalyzer, and ferritin, α-1 acid glycoprotein (AGP), and C-reactive protein (CRP) concentrations were measured with a sandwich-ELISA. Ferritin concentrations are presented as unadjusted and inflammation-adjusted (based on AGP and CRP concentrations). ANOVA and post-hoc pairwise t-tests were used to compare variables across trimesters of pregnancy. Mean±SD age of women was 26 ± 5 years. Most women (94%) reported consumption of iron and folic acid (IFA) tablets during pregnancy (mean±SD: 85 ± 19 tablets), and 72% received deworming treatment. Overall, 49% of women had anemia (hemoglobin <110 g/L for first and third trimesters; < 105 g/L for second trimester); with 43%, 34%, and 64% in the first, second and third trimester, respectively. ID prevalence (unadjusted ferritin <30 µg/L) ranged widely by trimester: 0%, 17% and 76% in the first, second and third trimester, as well as with use of a lower ferritin threshold (0-52%; < 15 µg/L), and whether ferritin was inflammation-adjusted (61% with and 43% without adjustment; < 30 µg/L). ID prevalence was high among women in third trimester, despite high IFA compliance. These findings underscore the need to consider the trimester of pregnancy in anemia and ID assessment. More research is needed to determine if trimester-specific thresholds for ferritin in pregnant populations are warranted and whether ferritin should be adjusted for inflammation in pregnancy.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".