Ideal Dietary Intakes of Vitamin B12 and Vitamin E Prevent Anemia during Pregnancy
Bibliographic record
Abstract
Anemia in pregnancy is one of the priority nutritional problems to be tended to. WHO in 2019 reported Anemia in 40% of pregnant women worldwide; in Indonesia, based on the 2018 Riskesdas was 48.9%, and in West Sumatra and Padang City, 18.10% and 17.70%. One of the causes of Anemia was a low intake of vitamins B12 and E. This study aimed to determine if serum ferritin levels and vitamin B12 and E consumption were correlated with those of third-trimester pregnant women. This analytical cross-sectional study was done from April to July 2022 at Lubuk Kilangan Public Health Center and Andalas University's Biomedical Laboratory. The population was 64 third-trimester pregnant women, and 42 samples were used using proportional stratified random and simple random sampling. A SQ-FFQ (Semi-Quantitative Food Frequency Questionnaire) and Human ferritin kit DBC (Diagnostics Biochem Canada) examined by ELISA (Enzymed-Linked Immunosorbent Assays) were the instruments. The average of Vitamin B12 and vitamin E consumption daily was 7.71 µg and 5.87 mg, and serum ferritin was 10.53 µg/L. Serum ferritin levels were linked with vitamin B12 (r=0.879; p=0.001) and vitamin E (r=0.455; p=0.002) intake. Enough intake of vitamin B12 and vitamin E will lead to ideal serum ferritin levels.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".