Iron deficiency and iron deficiency anaemia in women of reproductive age: Sex- and gender-based risk factors and inequities
Bibliographic record
Abstract
Iron deficiency (ID) is a serious public health problem that affects 20-25 % of the population and 52 % of pregnant people worldwide. Biologically female women (women) of reproductive age have a higher risk of developing ID due to the increased physiologic demand for iron required to support menstruation and pregnancy. If left untreated, ID can develop into iron deficiency anaemia (IDA), which affects one in three women between the ages of 15-49 years worldwide. Among women of reproductive age, those who are pregnant have the highest risk of developing ID and IDA due to increased iron requirements to support pregnancy and the developing fetus. Despite the high prevalence of ID and IDA, it remains underdiagnosed in reproductive-aged women and current treatment options are not well accepted. There is an urgent need to investigate novel strategies to ensure adequate iron status in women of reproductive age to prevent adverse health problems and promote healthy pregnancies. This review explored the critical role of iron in women's health by examining iron requirements throughout the lifespan, the physiology of iron absorption, factors affecting iron bioavailability, and the causes of ID and IDA. We discuss the limitations of current interventions for ID and IDA, and the need to develop effective and widely acceptable treatments for these conditions, particularly in women of reproductive age. The findings of this review suggest that current interventions for ID and IDA are inadequate and that sex biases exist in the diagnosis and management of ID and IDA in biologically female women.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".