The Incidence and Prevalence of Thyroid Disease Do Not Differ in the Multiple Sclerosis and General Populations: A Validation Study Using Administrative Data
Bibliographic record
Abstract
BACKGROUND: Prior studies of a possible increased risk of autoimmune thyroid disease (AIT) in multiple sclerosis (MS) are inconsistent. We aimed to validate and apply administrative case definitions for the surveillance of AIT in MS. METHODS: We used administrative health data to identify 4,192 persons with MS and an age-, sex- and geographically matched general population cohort (n = 20,940). We developed case definitions for AIT using International Classification of Disease-9/10 codes and prescription claims, compared them to medical records and applied them to estimate the incidence and prevalence of AIT. RESULTS: When compared to medical records, the administrative case definition using ≥1 hospital or ≥2 physician or ≥2 prescription claims had a sensitivity of 73.5% and specificity of 98.4%. In 2005, the age-adjusted prevalence of AIT was 9.51% [95% confidence interval (CI) 8.46-10.6] in the MS population and 8.56% (95% CI 8.11-9.02) in the general population. The age-adjusted incidence of AIT per 100,000 persons per year was 422.8 (95% CI 204.4-641.3) in the MS population and 407.7 (95% CI 308.5-506.9) in the general population. From 1996 to 2005, the prevalence of AIT rose in both populations. CONCLUSION: Administrative data can be used for surveillance of AIT in MS. The incidence and prevalence of thyroid disease are similar in the MS and general populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".