Preventing iron deficiency: Results of a randomized controlled trial of double‐fortified salt in female Indian tea pluckers
Bibliographic record
Abstract
Previous research has shown that consuming iron‐fortified salt improves iron status. The present analyses examined predictors of the prevention of iron deficiency (ID) during a randomized, double‐blinded, placebo controlled intervention of double‐fortified salt (DFS). Participants were 217 female tea pluckers, aged 19–55 years, from West Bengal, India. Mixed models were used to assess the effect of DFS on the change in baseline iron status and other micronutrient indicators (iodine, folate, vitamin B 12 ). Predictors of ID incidence were identified using logistic regression models. DFS significantly decreased the odds that women who had normal iron status at baseline would develop moderate or clinical ID (ferritin <20 or 12 μg/mL, respectively). Higher hemoglobin and lower transferrin receptor concentrations at baseline also predicted lower odds of developing ID or moderate ID. The results are discussed in the context of designing interventions in multiple‐micronutrient‐deficient settings and may help identify populations likely to benefit from future efficacy and effectiveness trials of dietary iron interventions. Supported by the Mathile Institute and the Micronutrient Initiative.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".