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Record W4307232309 · doi:10.1177/13524585221129963

Low socioeconomic status was associated with a higher mortality risk in multiple sclerosis

2022· article· en· W4307232309 on OpenAlexafffund
Floriane Calocer, Huah Shin Ng, Feng Zhu, Yinshan Zhao, Olivier Dejardin, Emmanuelle Leray, Gilles Defer, Charity Evans, John D. Fisk, Ruth Ann Marrie, Helen Tremlett

Bibliographic record

VenueMultiple Sclerosis Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of ManitobaDalhousie UniversityUniversity of SaskatchewanUniversity of British Columbia HospitalOkanagan University CollegeNova Scotia Health AuthorityUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health ResearchFondation pour l'Aide à la Recherche sur la Sclérose en Plaques
KeywordsSocioeconomic statusHazard ratioMedicineConfidence intervalProportional hazards modelMultiple sclerosisDemographyInternal medicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: The relationship between socioeconomic status (SES) and mortality among persons with multiple sclerosis (PwMS) is poorly understood. OBJECTIVE: To investigate the association between SES and mortality risk in PwMS. METHODS: From health-administrative data, we identified 12,126 incident MS cases with a first demyelinating event (MS 'onset') occurring between 1994 and 2017. Cox proportional hazard model assessed the association between socioeconomic status quintiles (SES-Qs) at MS onset and all-cause mortality. RESULTS: Lower SES-Qs were associated with higher mortality risk; adjusted hazard ratios: SES-Q1 (most deprived) =1.61 (95% confidence interval (CI) = 1.36-1.91); SES-Q2 = 1.26 (95% CI = 1.05-1.50); SES-Q3 = 1.22 (95% CI = 1.02-1.46); SES-Q4 = 1.13 (95% CI = 0.94-1.35) versus SES-Q5 (least deprived). CONCLUSION: A lower SES was associated with higher mortality risk in PwMS.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.115
GPT teacher head0.295
Teacher spread0.180 · 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.

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

Citations22
Published2022
Admission routes2
Has abstractyes

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