Accelerating action to reduce anemia: Review of causes and risk factors and related data needs
Bibliographic record
Abstract
Anemia is a major public health concern. Young children, menstruating adolescent girls and women, and pregnant women are among the most vulnerable. Anemia is the consequence of a wide range of causes, including biological, socioeconomic, and ecological risk factors. Primary causes include: iron deficiency; inherited red blood cell disorders; infections, such as soil-transmitted helminthiasis, schistosomiasis, and malaria; gynecological and obstetric conditions; and other chronic diseases that lead to blood loss, decreased erythropoiesis, or destruction of erythrocytes. The most vulnerable population groups in low- and middle-income countries are often at the greatest risk to suffer from several of these causes simultaneously as low socioeconomic status is linked with an increased risk of anemia through multiple pathways. Targeted and effective action is needed to prevent anemia. Understanding the causes and risk factors of anemia for different population subgroups within a country guides the design and implementation of effective strategies to prevent and treat anemia. A coordinated approach across various expert groups and programs could make the best use of existing data or could help to determine when newer and more relevant data may need to be collected, especially in countries with a high anemia burden and limited information on the etiology of anemia.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".