Application of the Dietary Reference Intakes in developing a recommendation for pregnancy iron supplements in Canada
Bibliographic record
Abstract
BACKGROUND: For many pregnant Canadian women, usual iron intakes from food appear to be inadequate compared with Dietary Reference Intake requirement estimates. OBJECTIVE: Dietary intake modeling was undertaken to determine an amount of iron supplementation that would confer acceptably low prevalence of apparently inadequate and apparently excessive intakes. DESIGN: The distribution of usual dietary iron intakes was estimated with the use of 24-h recalls from pregnant women aged 19-50 y in the Canadian Community Health Survey, Cycle 2.2. The prevalence of usual intakes below the Estimated Average Requirement for pregnancy (22 mg/d) or above the Tolerable Upper Intake Level (45 mg/d) was estimated. Iterative modeling with incremental iron supplement was performed to determine a suitable supplement amount. Because the sample of pregnant women was small (148 day 1 recalls), estimates of the tails of the distributions had large SDs, and supporting analyses based on intake data from nonpregnant women (4540 day 1 recalls) were made. RESULTS: Daily supplementation shifted the intake distribution curve without changing its shape. Supplementation with 16 mg iron/d was consistent with low (<3%) prevalence of apparently inadequate intakes. This amount of supplementation should not be associated with an increase in apparently excessive intakes by pregnant women in this population. CONCLUSIONS: On the basis of Dietary Reference Intakes, an iron supplement of 16 mg/d throughout pregnancy is justified as both efficacious and safe for healthy women living in Canadian households. This does not preclude the need for therapeutic iron doses for some individuals on the basis of iron status. The method can be applied to other populations if suitable baseline iron intake data are available.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".