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Record W2169269169 · doi:10.1136/jnnp.74.8.1128

Genetic loading in familial migraine with aura

2003· article· en· W2169269169 on OpenAlexaff
S E Noble-Topham

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2003
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsLondon Health Sciences CentreLawson Health Research Institute
Fundersnot available
KeywordsProbandAuraOffspringSiblingMedicineGrandparentFamily historyFirst generationPopulationDemographyMigraineGeneticsInternal medicinePsychologyBiologyPregnancyDevelopmental psychologyMutation

Abstract

fetched live from OpenAlex

Migraine with aura (MA) arises from a combination of genetic and environmental factors. The sibling risk, age at onset, and aura type were compared in 54 MA probands categorised by family history of MA. Three family types were ascertained each having an MA proband and: (1) an MA parent and MA offspring (three generation; n=15), (2) either an MA parent or an MA offspring (two generation; n=20), and (3) neither an MA parent nor an MA offspring (one generation; n=19). The crude recurrence risk to siblings of probands was 2.7-fold higher in three generation compared with two generation MA families (chi(2)=6.24, p=0.0125) and 4.8-fold higher in three generation compared with one generation MA families (chi(2)=9.95, p<0.002). The mean age at onset decreased with an increase in genetic load. The MA probands from three generation families were significantly younger than probands from the one generation families (F=5.14, p=0.030). MA probands from three generation families were more likely to report more than one type of aura than MA probands from two generation families (chi(2)=4.44, p=0.035). The significant difference in genetic loading and the earlier age at onset in the three generation families add further evidence for a genetic basis for MA and the difference in sibling risks demonstrates that the MA population is heterogeneous.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.016
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.252
Teacher spread0.238 · 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

Citations19
Published2003
Admission routes1
Has abstractyes

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