The Utility of Administrative Data for Surveillance of Comorbidity in Multiple Sclerosis: A Validation Study
Bibliographic record
Abstract
BACKGROUND: Although comorbidity is important in multiple sclerosis (MS), few validated methods for its assessment exist. We validated and applied administrative case definitions for several comorbidities in MS. METHODS: Using provincial administrative data we identified persons with MS and a matched general population cohort. Case definitions for chronic lung disease (CLD), epilepsy, inflammatory bowel disease (IBD), irritable bowel syndrome (IBS) and migraine were developed using administrative data, and validated against medical records. We applied these definitions to estimate the age-standardized prevalence of these comorbidities in the MS and matched cohorts. RESULTS: Versus medical records, administrative case definitions showed moderate agreement for CLD (ĸ = 0.41), migraine (ĸ = 0.51), and epilepsy (ĸ = 0.44), fair agreement for IBS (ĸ = 0.36) and could not be calculated for IBD (small sample size). The 2005 prevalence of CLD was similar in the MS (15.6%) and general populations (14.4%). The prevalence of the remaining comorbidities was higher in the MS than the general populations: epilepsy (4.12 vs. 1.12%), IBD (0.78 vs. 0.65%), IBS (12.2 vs. 6.80%) and migraine (23.0 vs. 16.5%). CONCLUSIONS: Administrative data are valid for tracking CLD, epilepsy, and migraine in MS. The prevalence of epilepsy, IBD, IBS and migraine is increased in MS versus the general population.
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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 | 0.032 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".