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Record W4417018565 · doi:10.1080/22423982.2025.2593698

A comparative cohort study of epilepsy in children in Greenland and Denmark

2025· article· en· W4417018565 on OpenAlexaboutno aff
Jacqueline M. Mistry, Bolette Søborg, Anders Koch, Mikael Andersson, María J. Miranda, Malene Landbo Børresen

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2025
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
FundersDanmarks Frie ForskningsfondMichaelsen FondenTorben og Alice Frimodts Fond
KeywordsEpilepsyDanishIncidence (geometry)Cohort studyEthnic groupCohortEpidemiology

Abstract

fetched live from OpenAlex

Few studies on epilepsy in Greenlandic children exist, and the results are inconsistent. The objective of this study was to estimate the burden of epilepsy in children in Greenland and Denmark and secondly to identify demographic risk factors. Third, the risk of epilepsy after febrile seizures should be estimated. A register-based cohort study of all children in Greenland and Denmark aged 0-15 years from 1987 to 2014 was conducted. Using the Greenlandic and Danish National Patient Register, cases were identified and coupled to demographics through the Civil Registration System. The outcomes were incidence rates (IR) per 100,000 person-years (PY) and hazard ratios (HR).The study showed an epilepsy IR of 150/100,000 PY (139-162) in Greenland and 110/100,000PY (108-111) in Denmark and an HR of 1.34. The IRs were very high in the first years of life. A significantly higher HR was found for male sex, Inuit ethnicity in Greenland and habitation in Greenland outside of the capital. The risk of epilepsy after the diagnosis of febrile seizures was almost doubled in Greenland compared with Denmark.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.175
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.385
Teacher spread0.364 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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