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Record W2101347270 · doi:10.1177/0009922809331800

The Association Between Iron Deficiency and Febrile Seizures in Childhood

2008· article· en· W2101347270 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Pediatrics · 2008
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineFebrile seizureEmergency departmentOdds ratioPediatricsRetrospective cohort studyConditional logistic regressionIron deficiencyAnemiaIron-deficiency anemiaLogistic regressionCohortCohort studyInternal medicineEpilepsyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to determine the association between iron deficiency and febrile seizures in a large cohort of children aged 6 to 36 months. METHODS: A retrospective case control study with 361 patients who presented with febrile seizures to the emergency department and 390 otherwise healthy controls who presented with a febrile illness to the emergency department were reviewed to determine iron status using the MCV, RDW, and hemoglobin. RESULTS: A total of 9% of cases had iron deficiency (ID) and 6% had iron deficiency anemia (IDA), compared to 5% and 4% of controls respectively. The conditional logistic regression odds ratio for ID in patients with febrile seizures was 1.84 (95% CI, 1.02-3.31). CONCLUSION: Children with febrile seizures were almost twice as likely to be iron deficient as those with febrile illness alone. The results suggest that screening for ID should be considered in children presenting with febrile seizure.

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.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.011
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.046
GPT teacher head0.363
Teacher spread0.317 · 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