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Record W2906257880 · doi:10.1007/s00415-018-9163-6

Familial risk of early- and late-onset multiple sclerosis: a Swedish nationwide study

2018· article· en· W2906257880 on OpenAlexfundno aff
Jie Song, Helga Westerlind, Kyla A. McKay, Catarina Almqvist, Pernilla Stridh, Ingrid Kockum, Jan Hillert, Ali Manouchehrinia

Bibliographic record

VenueJournal of Neurology · 2018
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersHorizon 2020Canadian Institutes of Health ResearchEuropean Committee for Treatment and Research in Multiple Sclerosis
KeywordsMultiple sclerosisMedicineAge of onsetPediatricsOdds ratioNeurologyFamily aggregationPopulationYoung adultCase-control studyEpidemiologyInternal medicineDiseasePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Persons who develop multiple sclerosis (MS) at a young age may bear a higher genetic risk load than persons who develop MS later in life; however, the contribution of familial influence to the risk of MS, in relation to onset age, has not been established. OBJECTIVE: To investigate the familial risk of MS at two extremes of the spectrum of MS onset age: early onset (first MS symptom < 18 years of age) and late onset (first MS symptom ≥ 50 years). METHODS: Nationwide registries in Sweden were used to identify cases of MS and controls, and their familial relations. We estimated the odds ratio (OR) of an MS diagnosis for individuals with a relative diagnosed with early-onset or late-onset MS compared with those whose relatives did not have MS, using a nested case-control design. RESULTS: 629 early-onset and 1148 late-onset MS patients were identified and matched to 10 controls from the general population by age and sex. The OR of MS for individuals with a first-degree relative diagnosed with early-onset MS was 10.86 (95% CI 6.87-17.17); and for late-onset MS was 8.08 (95% CI 6.12-10.67). CONCLUSIONS: Our findings demonstrate no substantial differences in familial risk in persons with early- and late-onset MS.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.066
GPT teacher head0.321
Teacher spread0.255 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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