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Anticipation of age at onset in familial multiple sclerosis

2009· article· en· W2127477301 on OpenAlexaff
Sergio Martínez‐Yélamos, L. Gubieras, Elisabet Matas, Laura Bau, Marcelo Kremenchutzky, T. Arbizu

Bibliographic record

VenueEuropean Journal of Neurology · 2009
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsWestern University
FundersSanofi
KeywordsMedicineAnticipation (artificial intelligence)Age of onsetPediatricsMultiple sclerosisCohortFamily aggregationPopulationDiseaseInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: Anticipation of age at onset in the younger generations is a widely known characteristic of many diseases with genetic inheritance. This study was performed to assess whether there is anticipation of age at onset in younger generations of familial multiple sclerosis (MS) in a Spanish population and to compare clinical characteristics of familial and sporadic MS. METHODS: We studied a cohort of 1110 patients diagnosed with MS and followed-up in our MS Unit. Patients were considered as familial MS if they had in their family at least one relative of first or second degree diagnosed with MS. Otherwise, patients were considered to have sporadic MS. We compared the age at onset between relatives from different generations, and we also compared the age at onset of familial and sporadic MS. RESULTS: A lower age at onset in the younger generations was found (median 22 years vs. 30 years, P < 0.001) and a significant lower age at onset of the disease in familial MS comparing to sporadic MS (median 25 years vs. 29 years, P = 0.042). CONCLUSIONS: There is an anticipation of the age at onset of MS in the younger generations of patients with familial MS. There is also a lower age at onset in familial versus sporadic 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.109
GPT teacher head0.316
Teacher spread0.207 · 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

Citations30
Published2009
Admission routes1
Has abstractyes

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