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Record W2148180068 · doi:10.5539/gjhs.v8n2p185

The Relationship Between Iron Deficiency and Febrile Convulsion: A Case-Control Study

2015· article· en· W2148180068 on OpenAlexvenueno aff
Mohammad Reza Sharif, Davood Kheirkhah, Mahla Madani, Hamed Haddad Kashani

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsConvulsionMedicineFebrile seizureIron-deficiency anemiaAnemiaIron deficiencyPediatricsSeizure DisordersSerum ironFebrile convulsionsAnesthesiaEpilepsyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.377
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2015
Admission routes1
Has abstractyes

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