Iron status of adolescent girls from two boarding schools in southern Benin
Bibliographic record
Abstract
Iron deficiency is the most prevalent micronutrient deficiency in the world, particularly in developing countries. Blood samples and a qualitative food frequency questionnaire on iron and vitamin C rich foods were obtained in 180 adolescent girls aged 12 to 17 years living in two boarding schools from South Benin. Iron deficiency, defined as either serum ferritin <20 μg/L or serum ferritin 20–50 μg/L plus two of the following parameters: serum iron <11 μmol/L, total iron binding capacity >73 μmol/L or transferrin saturation <20%, was found in 32% of subjects. Anemia (hemoglobin (Hb) <120 g/L) was found in 51% of adolescents, while 24% suffered from iron deficiency anemia (iron deficiency and Hb <120 g/L). After adjusting for confounding factors (age, mother's and father's occupation, household size) in a logistic regression equation, subjects having a low meat consumption (beef, mutton, pork) (<4 times a week) were more than twice as likely to suffer from ID (odd ratio (OR) 2.43 and 95% confidence interval (CI) 1.72–3.35; P=0.04). Adolescents consuming less fruits (<4 times a week) also had a higher likelihood of suffering from ID (OR=1.53; 95%CI=1.31–2.80; P=0.03). Finally, subjects whose meat consumption was low, were twice as likely to suffer from iron deficiency anemia (OR=2.24; 95%CI=1.01–4.96; P=0.04). The prevalence of iron deficiency represents an important health problem in these Beninese adolescent girls. A higher consumption of iron rich foods and of promotors of iron absorption (meat factor and vitamin C) is recommended to prevent iron deficiency in these subjects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".