Association Between Iron-Deficiency Anemia and Stroke in Young Children
Bibliographic record
Abstract
OBJECTIVE: Iron-deficiency anemia occurs with a peak prevalence of 4% to 8% in children between 1 and 3 years of age. Case reports have suggested an association between iron-deficiency anemia in healthy children and ischemic stroke. Our objective was to investigate whether iron-deficiency anemia is associated with stroke in young children. METHODS: A case-control study was conducted of case patients who were selected from the stroke registry at the Hospital for Sick Children (Toronto, Ontario, Canada) and control subjects selected from a database of healthy children who were prospectively enrolled in an outpatient setting. Children were aged 12 to 38 months and were previously healthy with no identifiable risk factors for stroke. Age, gender, mean corpuscular volume, platelet count, and hemoglobin and ferritin levels were collected. Iron-deficiency anemia was defined as a hemoglobin level of <110 g/L, mean corpuscular volume <73 fL, and serum ferritin level <12 microg/L. Stroke was defined according to clinical and radiologic criteria. RESULTS: Case (n = 15) and control (n = 143) subjects were similar with respect to median age and percentage of boys. Case patients had a lower median hemoglobin level and mean corpuscular volume and a higher median platelet count. Iron-deficiency anemia was significantly more common among case patients (8 [53%] of 15) than control subjects (13 [9%] of 143). CONCLUSIONS: Previously healthy children with stroke were 10 times more likely to have iron-deficiency anemia than healthy children without stroke. Furthermore, children with iron-deficiency anemia accounted for more than half of all stroke cases in children without an underlying medical illness, which suggests that iron-deficiency anemia is a significant risk factor for stroke in otherwise healthy young children. Primary prevention and early identification of iron-deficiency anemia must remain a priority.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".