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Environmental risk factors in multiple sclerosis

2008· review· en· W1991249712 on OpenAlexaff
Maura Pugliatti, Hanne F. Harbo, Trygve Holmøy, Margitta T. Kampman, Kjell‐Morten Myhr, Trond Riise, Christina Wolfson

Bibliographic record

VenueActa Neurologica Scandinavica · 2008
Typereview
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsEpidemiologyMultiple sclerosisDiseaseGenetic epidemiologyCohort studyPopulationMedicineIncidence (geometry)Case-control studyCohortEnvironmental healthBiologyDemographyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Multiple sclerosis (MS) likely results from an interaction between genetic and exogenous factors. While genetics shapes the overall population MS susceptibility, observed epidemiological patterns strongly suggest a role for the environment in disease initiation and modulation. RESULTS: Findings from studies on seasonality in MS patients' birth, disease onset and exacerbations, as well as apparent temporal trends in incidence and gender ratio support an influential effect of viruses, metabolic and lifestyle factors on MS risk. Epstein-Barr virus, vitamin D status, and smoking are factors that may explain such epidemiological patterns. CONCLUSIONS: Further epidemiological investigations are encouraged and opportunities to use data from existing cohort studies as well as the design of new studies should be pursued. In particular, the development of new large multicentre population-based case-control studies which incorporate the study of the role of environment and genetics, including epigenetic mechanisms, in determining MS risk is proposed.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.160
GPT teacher head0.325
Teacher spread0.165 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations187
Published2008
Admission routes1
Has abstractyes

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