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Record W4223597535 · doi:10.1212/wnl.0000000000200545

Association of Latitude and Exposure to Ultraviolet B Radiation With Severity of Multiple Sclerosis

2022· article· en· W4223597535 on OpenAlexfundno aff
Marianna Vitková, Ibrahima Diouf, Charles B. Malpas, Dana Horáková, Eva Havrdová, Francesco Patti, Serkan Özakbaş, Guillermo Izquierdo, Sara Eichau, Vahid Shaygannejad, Marco Onofrj, Alessandra Lugaresi, Raed Alroughani, Alexandre Prat, Catherine Larochelle, Marc Girard, Pierre Duquette, Murat Terzi, Cavit Boz, François Grand’Maison, Patrizia Sola, Diana Ferraro, Pierre Grammond, Helmut Butzkueven, Katherine Buzzard, Olga Skibina, Bassem Yamout, Rana Karabudak, Oliver Gerlach, Jeannette Lechner‐Scott, Davide Maimone, Roberto Bergamaschi, Vincent Van Pesch, Gerardo Iuliano, Elisabetta Cartechini, María José Sá, Radek Ampapa, Michael Barnett, Stella E. Hughes, Cristina Ramo‐Tello, Suzanne Hodgkinson, Daniele Spitaleri, Thor Petersen, Ernest Butler, Mark Slee, Chris McGuigan, Pamela McCombe, Franco Granella, Edgardo Cristiano, Julie Prévost, Bruce Taylor, José Luis Sánchez-Menoyo, Guy Laureys, Liesbeth Van Hijfte, Steve Vucic, Richard Macdonell, Orla Gray, Javier Olascoaga, Norma Deri, Yára Dadalti Fragoso, Cameron Shaw, Tomáš Kalinčík

Bibliographic record

VenueNeurology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
FundersUniversity of TasmaniaMultiple Sclerosis Society of CanadaTeva Pharmaceutical IndustriesCelgeneFondazione Italiana Sclerosi MultiplaMylanBiogenSanofi
KeywordsMultiple sclerosisUltraviolet radiationAssociation (psychology)MedicineChemistryImmunologyPsychologyRadiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The severity of multiple sclerosis (MS) varies widely among individuals. Understanding the determinants of this heterogeneity will help clinicians optimize the management of MS. The aim of this study was to investigate the association between latitude of residence, UV B radiation (UVB) exposure, and the severity of MS. METHODS: This observational study used the MSBase registry data. The included patients met the 2005 or 2010 McDonald diagnostic criteria for MS and had a minimum dataset recorded in the registry (date of birth, sex, clinic location, date of MS symptom onset, disease phenotype at baseline and censoring, and ≥1 Expanded Disability Status Scale score recorded). The latitude of each study center and cumulative annualized UVB dose at study center (calculated from National Aeronautics and Space Administration's Total Ozone Mapping Spectrometer) at ages 6 and 18 years and the year of disability assessment were calculated. Disease severity was quantified with Multiple Sclerosis Severity Score (MSSS). Quadratic regression was used to model the associations between latitude, UVB, and MSSS. RESULTS: The 46,128 patients who contributed 453,208 visits and a cumulative follow-up of 351,196 patient-years (70% women, mean age 39.2 ± 12 years, resident between latitudes 19°35' and 56°16') were included in this study. Latitude showed a nonlinear association with MS severity. In latitudes <40°, more severe disease was associated with higher latitudes (β = 0.08, 95% CI 0.04-0.12). For example, this translates into a mean difference of 1.3 points of MSSS between patients living in Madrid and Copenhagen. No such association was observed in latitudes <40° (β = -0.02, 95% CI -0.06 to 0.03). The overall disability accrual was faster in those with a lower level of estimated UVB exposure before the age of 6 years (β = - 0.5, 95% CI -0.6 to 0.4) and 18 years (β = - 0.6, 95% CI -0.7 to 0.4), as well as with lower lifetime UVB exposure at the time of disability assessment (β = -1.0, 95% CI -1.1 to 0.9). DISCUSSION: In temperate zones, MS severity is associated with latitude. This association is mainly, but not exclusively, driven by UVB exposure contributing to both MS susceptibility and severity.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.078
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.029
GPT teacher head0.263
Teacher spread0.234 · 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

Citations33
Published2022
Admission routes1
Has abstractyes

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