WED 167 Socioeconomic status and progression of disability in ms
Bibliographic record
Abstract
There is evidence that socioeconomic status (SES) is associated with multiple sclerosis (MS) incidence; however it is less clear whether there is also an association with long-term prognosis. 3113 patients were selected from the MS registries of British Columbia, Canada (n=2069), and Cardiff, Wales (n=1044). SES, based on neighbourhood-level average income, was measured at onset of MS. Cox proportional hazards regression was used to analyse the association of SES with time to sustained and confirmed EDSS 6.0 and EDSS 4.0. The association between SES and EDSS scores was assessed longitudinally by a linear regression model fitted using generalised estimating equations (GEE) with an exchangeable working correlation structure. All models were adjusted for age at onset, sex, year of onset, initial course and DMT. The cohorts were analysed individually and results combined using meta-analysis. SES was associated with hazard of reaching EDSS 6.0 (adjusted hazard ratio [aHR]=0.90, 95% CI 0.89–0.91), and 4.0 (aHR=0.93, 0.88–0.98). GEE modelling confirmed association of SES with EDSS (β=−0.13, [−0.18- −0.08], p<0.001). We found evidence that lower SES is associated with poorer outcomes. Reasons for this are complex but may include lifestyle or comorbidity. Our findings are relevant for planning and development of MS services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".