Review of intervention products for use in the prevention and control of anemia
Bibliographic record
Abstract
Anemia remains a major public health problem, especially in low- and middle-income countries. The World Health Organization recommends several interventions to prevent and manage anemia in vulnerable population groups, including young children, menstruating adolescent girls and women, and pregnant and postpartum women. Daily iron supplementation reduces the risk of anemia in infants, children, and pregnant women, and intermittent iron supplementation reduces anemia risk in menstruating girls and women. Micronutrient powders reduce the risk of anemia in children. Fortifying wheat flour with iron reduces the risk of anemia in the overall population, whereas the effect of fortifying maize flour and rice is still uncertain. Regarding non-nutrition-related interventions, malaria treatment and deworming have been reported to decrease anemia prevalence. Promising interventions to prevent anemia include vitamin A supplementation, multiple micronutrient supplementation for pregnant women, small-quantity lipid-based supplements, and fortification of salt with iodine and iron. Future research could address the efficacy and safety of different iron supplementation formulations, identify the most bioavailable form of iron for fortification, examine adherence to supplementation regimens and fortification standards, and investigate the effectiveness of integrating micronutrient, helminth, and malaria control programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".