Management of vascular risk in people with multiple sclerosis at the time of diagnosis in England: A population-based study
Bibliographic record
Abstract
BACKGROUND: Vascular management in People with Multiple Sclerosis (PwMS) is important given the higher vascular burden than the general population, associated with increased disability and mortality. OBJECTIVES: We assessed differences in the prevalence of type 2 diabetes and hypertension; and the use of antidiabetic, antihypertensive and lipid-lowering medications at the time of the MS diagnosis. METHODS: This is a population-based study including PwMS and matched controls between 1987 and 2018 in England. RESULTS: We identified 12,251 PwMS and 72,572 matched controls. PwMS had a 30% increased prevalence of type 2 diabetes (95% confidence interval (CI) = 1.19, 1.42). Among those with type 2 diabetes, PwMS had a 56% lower prevalence of antidiabetic usage (95% CI = 0.33, 0.58). Prevalence of hypertension was 6% greater in PwMS (95% CI = 1.05, 1.06), but in those with hypertension, usage of antihypertensive was 66% lower in PwMS (95% CI = 0.28, 0.42) than controls. Treatment with lipid-lowering medications was 63% lower in PwMS (95% CI = 0.54, 0.74). PwMS had a 0.4-mm Hg lower systolic blood pressure (95% CI = -0.60, -0.13). 3.8% of PwMS were frail. CONCLUSION: At the time of diagnosis, PwMS have an increased prevalence of vascular risk factors, including hypertension and diabetes though paradoxically, there is poorer treatment. Clinical guidelines supporting appropriate vascular assessment and management in PwMS should be developed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".