Iron Status, Anemia, and Iron Interventions and Their Associations with Cognitive and Academic Performance in Adolescents: A Systematic Review
Bibliographic record
Abstract
In adolescents, iron-deficiency anemia is the leading cause of disability-adjusted life years lost. The World Health Organization recommends delivering iron supplementation through school-based platforms, requiring partnerships with the education sector. This anemia-reduction intervention is valued for the perceived benefits of improved learning and school performance. This article aims to systematically review the available evidence on the relationship between iron status and anemia and impacts of iron interventions on cognitive and academic performance in adolescents. Fifty studies were included: n = 26 cross-sectional and n = 24 iron-containing interventions. Our review suggests that iron status and anemia may be associated with academic performance in some contexts and that iron supplementation during adolescence may improve school performance, attention, and concentration. However, nearly all supplementation trials were judged to have moderate or high risk of bias. We did not find evidence suggesting that iron status and anemia influenced or were associated with attention, intelligence, nor memory in adolescents. Further, iron supplementation did not improve memory and recall or intelligence. Overall, more high-quality research is needed to guide programmers and policy makers to understand the relationships between anemia and educational performance and the potential impacts of iron interventions, which effectively reduce anemia, on adolescents’ learning and school performance.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".