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Record W2008061132 · doi:10.1159/000072927

Multiple Sclerosis in Northern Sardinia, Italy: A Methodological Approach for Genetic Epidemiological Studies

2003· article· en· W2008061132 on OpenAlexaff
Maura Pugliatti, Stefano Sotgiu, A. Dessa Sadovnick, Irene M. Yee, Maria Alessandra Sotgiu, Charles M. Poser, Giulio Rosati

Bibliographic record

VenueNeuroepidemiology · 2003
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of British Columbia
FundersMinistero dell'Università e della RicercaFondazione Italiana Sclerosi Multipla
KeywordsMedicineEpidemiologyMultiple sclerosisDiseasePopulationIncidence (geometry)DemographyFamily historyFamily aggregationPediatricsEnvironmental healthPathologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The etiopathogenesis of multiple sclerosis (MS) remains unclear. However, genetic factors are believed to be important in disease susceptibility. A methodological approach is presented for a population-based study aimed at investigating MS familial incidence and patterns of familial clustering in Northern Sardinia, Italy, with a reported MS prevalence of 150/100,000 population. Patients with MS since 1965 to the present and known to the MS Register for the province of Sassari will be asked to provide genealogical, comorbid and demographic information. Statistical analyses of the familial risk for MS will depend on the completeness of MS 'age at onset' data for affected individuals and age at the time family history is obtained (or age at death) for unaffected family members.

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)0.181
metaresearch head score (Gemma)0.260
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: none
Teacher disagreement score0.181
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.260
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.008
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.577
GPT teacher head0.455
Teacher spread0.122 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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