Association of simple febrile seizure with iron deficiency anemia in children in a tertiary hospital in Gazipur, Bangladesh
Bibliographic record
Abstract
Background: Simple febrile seizures represent the most frequent type of seizure disorder in early childhood, with multifactorial etiologies including nutritional and metabolic imbalances. Among these, iron deficiency anemia (IDA) has emerged as a possible modifiable risk factor, though evidence remains varied across populations and clinical settings. Aim of the study: To investigate the association between iron deficiency anemia and simple febrile seizures among children presenting to a tertiary hospital in Gazipur, Bangladesh. Methods: A case-control study was conducted over a one-year period, involving 80 children aged 6 months to 6 years. Fourty (40) children with a diagnosis of simple febrile seizure constituted the case group, while 40 age- and sex-matched children with febrile illness but no seizure history served as controls. Comprehensive hematological assessments, including hemoglobin, serum ferritin, mean corpuscular volume (MCV), and serum iron levels were performed. Statistical analyses were conducted using appropriate parametric and non-parametric tests, with a significance level set at p< 0.05. Result: Children with simple febrile seizures exhibited significantly lower levels of hemoglobin (mean: 9.5 ± 1.0 g/dL), serum ferritin (10.6 ± 4.3 ng/mL), and MCV (66.0 ± 4.6 fL) compared to controls (11.0 ± 1.1 g/dL, 22.0 ± 5.8 ng/mL, and 77.0 ± 5.1 fL, respectively; p< 0.001 for all). The prevalence of iron deficiency anemia was markedly higher among cases than controls (72.5% vs. 25%), with an odds ratio of 7.91 (95% CI: 2.77-22.7). Conclusion: Our study highlights a significant association between iron deficiency anemia and simple febrile seizures in children. Northern International Medical College Journal Vol. 16 No. 1-2 July 2024-January 2025, Page 745-749
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".