Association between febrile seizures and iron deficiency anemia in infants and preschool children: a Meta-analysis
Bibliographic record
Abstract
Objective To determine the relationship between febrile seizures and in infants and preschool children by using a meta-analysis of published articles. Methods The Chinese and English electronic databases were used in this study, such as PubMed, Web of science, MEDLINE, Wanfang Data Knowledge Service Platform, China Knowledge Network Database (CNKI), VIP Database, and Cochrane Library Database, collected from January 1990 to September 2017. The published articles about the association between febrile seizures and in infants and preschool children between 3 months and 6 years old were reviewed. The search terms in Chinese were deficiency or serum iron or serum ferritin or transferrin or anemia and hot convulsion ; the English search terms were anemia, iron-deficiency and seizures, febrile or pyrexial seizure or pyrexial convulsion. The literature was screened and extracted by two investigators according to the inclusion and exclusion criteria. The Newcastle-Ottawa Scale (NOS) was used for literature quality assessment. Stata SE 12.0 software was used for meta-analysis of whether was a risk factor for children with febrile seizures, calculation of odds ratio (OR), and subgroup analysis based on different diagnostic criteria for anemia. Results A total of 89 relevant literatures were detected in this study. After screening, 26 articles that met the inclusion and exclusion criteria were finally included for meta-analysis. The results of the analysis showed that febrile seizures were associated with [OR=2.24, 95% CI (1.758, 2.854), P<0.05]. Conclusion Iron is one of the risk factors for febrile seizures in infants and preschool children. Key words: Iron anemia; Febrile convulsions; Infant; Preschool 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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".