Dietary intake in women consuming three supplements with identical micronutrient content
Bibliographic record
Abstract
Objective to estimate energy, protein, iron, zinc, and folate intake from diet only and diet+supplement in women participants in a randomized controlled supplementation trial in Mexico. Methods Communities randomly assigned (18/suppl) to receive a fortified beverage (FB), micronutrient tablet, or micronutrient powder. Pregnant women (<25 wks), beneficiaries of Oportunidades were eligible to participate. Dietary intake was assessed by food frequency questionnaire (FFQ, previous month) 37 wks preg, 1 and 3 mo postpartum. Supplements were delivered and consumption estimated by observation (first 9 mo) then weekly (maternal report) for the rest of the trial. Comparisons among supplementation groups were using statistical analyses for cluster randomized trials. Results 694 women began supplementation (FB =224, T=235, MNP=235) and 571 (82%) finished the study; there were no significant differences among groups at baseline. Total protein intake from diet+supplements was higher in the FB (p<0.05), with no difference in diet or total energy or micronutrients. Conclusions Energy intake is high and likely overestimated by our FFQ; but we have no reason to believe that overestimation varies by group. The additional protein from FB in this population may not be needed as dietary intakes appear adequate. The Oportunidades program financed this study and holds the rights to the data presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".