Rising prevalence of vascular comorbidities in multiple sclerosis: validation of administrative definitions for diabetes, hypertension, and hyperlipidemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it