The Relationship Between Iron Deficiency and Febrile Convulsion: A Case-Control Study
Bibliographic record
Abstract
INTRODUCTION: Febrile seizure is among the most common convulsion disorders in children, which strikes 2% to 5% of children between 3 to 60 months of age. Some studies have reported that iron deficiency could be a risk factor for febrile seizure. The present study was conducted to compare the rate of iron deficiency anemia in febrile children with and without seizure. MATERIALS AND METHODS: This case-control study evaluated 200 children aged 6-60 month in two 100 person groups (febrile seizure and febrile without convulsion) in Kashan. The CBC diff, serum iron and TIBC were done for all of participants. Diagnosis of iron deficiency anemia based on mentioned tests. RESULTS: No significant differences were found in two groups regarding to the age, gender, and the disease causing the fever. The presence of iron deficiency anemia was 45% in the convulsion group and 22% in the group with fever without convulsion. The Chi Square test indicated a significant difference between two groups. CONCLUSIONS: The findings suggest that a considerable percentage of children having febrile seizure suffer from iron-deficiency anemia and low serum iron. This means the low serum iron and presence of anemia can serve as a reinforcing factor for the febrile seizure in children.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".