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Environmental risk factors in multiple sclerosis

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.160
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