Is cooking food in iron pots an appropriate solution for the control of anaemia in developing countries? A randomised clinical trial in Benin
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of two types of iron pots on haemoglobin (Hb) and serum ferritin (SF) concentrations in young children (6-24 months), adolescent girls (11-15 years) and women of reproductive age (15-44 years), whose households were provided with iron pots for cooking food over a period of 6 months, compared with controls. DESIGN AND METHODS: We randomly assigned 161 households including 339 individuals from the three subgroups to cast iron pots, blue steel pots or oral iron supplements (control). In the control group, children received micronutrient Sprinkles, and adolescent girls and women received iron tablets daily for 6 months. We measured Hb, SF and C-reactive protein concentrations at baseline and 6 months, and compared groups using regression models. RESULTS: Anaemia prevalence (Hb < 110 g l(-1) in infants, Hb < 120 g l(-1) in girls or women) was 47% (cast iron group), 50% (blue steel group) and 50% (control) at baseline. At 6 months, there were no significant differences in Hb concentrations among groups; however, differences in SF concentrations were significant (P < 0.0001)--the control group had higher SF concentrations compared with the groups using iron pots. Also, differences in the prevalence of iron-deficiency anaemia (IDA; anaemia plus SF < 15 microg l(-1)) were almost significant between cast iron and control groups (P = 0.08), and blue steel and control groups (P = 0.05). CONCLUSION: There is no evidence that iron cooking pots are effective against IDA. Further research is warranted to determine whether the iron leached from the pots is bioavailable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".