Rising prevalence of vascular comorbidities in multiple sclerosis: validation of administrative definitions for diabetes, hypertension, and hyperlipidemia
Bibliographic record
Abstract
BACKGROUND: Despite the importance of comorbidity in multiple sclerosis (MS), methods for comorbidity assessment in MS are poorly developed. OBJECTIVE: We validated and applied administrative case definitions for diabetes, hypertension, and hyperlipidemia in MS. METHODS: Using provincial administrative data we identified persons with MS and a matched general population cohort. Case definitions for diabetes, hypertension, and hyperlipidemia were derived using hospital, physician, and prescription claims, and validated in 430 persons with MS. We examined temporal trends in the age-adjusted prevalence of these conditions from 1984-2006. RESULTS: Agreement between various case definitions and medical records ranged from kappa (κ) =0.51-0.69 for diabetes, κ =0.21-0.71 for hyperlipidemia, and κ =0.52-0.75 for hypertension. The 2005 age-adjusted prevalence of diabetes was similar in the MS (7.62%) and general populations (8.31%; prevalence ratio [PR] 0.91; 0.81-1.03). The age-adjusted prevalence did not differ for hypertension (MS: 20.8% versus general: 22.5% [PR 0.91; 0.78-1.06]), or hyperlipidemia (MS: 13.8% versus general: 15.2% [PR 0.90; 0.67-1.22]). The prevalence of all conditions rose in both populations over the study period. CONCLUSION: Administrative data are a valid means of tracking diabetes, hypertension, and hyperlipidemia in MS. The prevalence of these comorbidities is similar in the MS and general populations.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".