Appendix 15: Iron-deficiency anemia: evidence review for newly a rriving immigrants and refugees
Bibliographic record
Abstract
Background: Iron-deficiency anemia is the most common nutritional disorder in the world. Subgroups of immigrants and refugees have higher prevalence of iron-deficiency anemia than the Canadian-born population has. Growing children and women of reproductive age are at highest risk for iron deficiency and related morbidity. We conducted an evidence review to identify actions to be taken by primary care practitioners to prevent morbidity from iron-deficiency anemia among newly arriving immigrants and refugees. Methods: We systematically assessed evidence on the screening and treatment of iron-deficiency anemia including benefits and harms, applicability, clinical considerations, and implementation issues. The quality of the evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: Prevalence of anemia is high in subgroups of newly arriving immigrants and refugees (women >15% and children >20%). Screening and treating iron-deficiency anemia in children can improve cognitive development to a modest degree. Screening and treating female patients of reproductive age can improve hemoglobin and function (work productivity). Iron-deficiency anemia in children is often a combination of inadequate diet, low iron stores at birth, and frequent infections leading to anorexia and poor food intake. High parity, malaria, and hemoglobinopathies increase the risk of anemia. Interpretation: Immigrant and refugee children and women of reproductive age are vulnerable to iron-deficiency anemia. Key interventions to detect, treat and prevent reoccurrences include measuring hemoglobin levels and recommending iron supplements and other dietary modifications. A culturally responsive nutrition assessment and counseling, when available, can identify specific nutritional issues and support appropriate diet modifications.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.006 |
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".