Iron-deficiency anaemia in rural Cambodia: community trial of a novel iron supplementation technique
Bibliographic record
Abstract
BACKGROUND: More than 3.5 billion people are affected by iron-deficiency anaemia (IDA). Previous studies have shown that the use of iron pots in daily cooking ameliorates IDA. We report a study on the use of a novel treatment to address IDA in rural women in Cambodia, where the use of iron pots is not common. METHODS: A community-wide randomized controlled trial was conducted in the village of Preak Ruessei, Kandal Province, Cambodia. Rural women (n = 189) were enrolled and randomly assigned by household to one of three groups: (i) control, (ii) iron treatment with no follow-up and (iii) iron treatment with follow-up visits to provide IDA education. Haemoglobin, serum iron and C-reactive protein concentrations were measured at baseline, 3 and 6 months. A reusable fish-shaped iron ingot was distributed to the two treatment groups and participants were directed to use them daily for cooking. We hypothesized that iron from the ingot would leach iron into food providing an effective iron source. RESULTS: Blood iron levels were higher in women in the iron fish plus follow-up at 3 months compared with controls, but this was not maintained. At 6 months, haemoglobin and serum iron had fallen in all groups and the proportion of anaemic women had increased. CONCLUSIONS: This study shows that the iron ingot was effective in the short but not longer-term against IDA. Though a novel treatment option, further research is warranted to determine bioavailability of leached iron and whether or not the surface area is large enough for sufficient iron leaching.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".