Predicting potential to benefit from an iron intervention: a randomized controlled trial of double-fortified salt in female Indian tea pluckers
Bibliographic record
Abstract
OBJECTIVE: The present study examines characteristics of those who benefited from a dietary Fe intervention comprised of salt double-fortified with iodine and Fe (DFS). DESIGN: Data from a randomized controlled trial were analysed to identify predictors of improved Fe status and resolution of Fe deficiency (serum ferritin (sFt) < 12 μg/l) and low body Fe (body Fe (BI) < 0·0 mg/kg) using non-parametric estimations and binomial regression models. SETTING: A tea estate in West Bengal, India. PARTICIPANTS: Female tea pluckers, aged 18-55 years. RESULTS: Consuming DFS significantly (P = 0·01) predicted resolution of Fe deficiency (relative risk (RR) = 2·31) and of low BI (RR = 2·78) compared with consuming iodized salt. Baseline sFt (β = -0·32 (se 0·03), P < 0·001) and treatment group (β = 0·13 (se 0·03), P < 0·001) significantly predicted change in sFt. The interaction of baseline BI with treatment group (β = -0·11 (se 0·06), P = 0·08) predicted the change in BI. DFS did not significantly predict change in Hb and marginally predicted resolution of anaemia (Hb < 120 g/l). CONCLUSIONS: Baseline Fe status, as assessed by sFt and BI, and consumption of DFS predict change in Fe status and resolution of Fe deficiency and low BI. Anaemia prevalence and Hb level, although simple and inexpensive to measure, may not be adequate to predict resolution of Fe deficiency in response to an intervention of DFS in similar populations with high prevalence of Fe deficiency and multiple nutritional causes of anaemia. These findings will guide appropriate targeting of future interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".