Interrogation of the Multiple Sclerosis Prodrome Using High-Dimensional Health Data
Bibliographic record
Abstract
BACKGROUND: There is growing evidence of a prodromal period in multiple sclerosis (MS). A better understanding of the prodrome may facilitate prompt recognition and treatment of MS as well as narrowing of the etiologically relevant -period when searching for MS risk factors. OBJECTIVES: To explore and further delineate the MS prodrome, we used statistical learning techniques to examine associations of physician-generated diagnostic codes and prescription medication classes in the 5 years before the first demyelinating-related claim for MS cases and matched population controls. METHODS: In this matched cohort study, we accessed data from linked health administrative hospital, physician, and prescription databases from British Columbia, Canada, between 1996 and 2013. We focused on 7 medication classes previously identified as associated with the MS prodrome: urinary anti-spasmodics, glucocorticoids, muscle relaxants, anti-epileptics, dopa-derivatives, benzodiazepine, and antivertigo preparations. Diagnostic codes associated with the use of each medication class were first identified using LASSO logistic regression analyses in two-thirds of the cohort and then validated using multivariate logistic regressions in the remaining cohort. RESULTS: Our analyses included 4,862 MS cases and 22,649 controls. Although the identified diagnostic codes showed fair to good predictive performance in 6 medication classes (C-index = 0.712-0.858), these codes failed to fully explain the higher usage of these medications by the MS cases. Compared to controls of the same age, sex, and diagnostic codes, MS cases had higher odds of filling a prescription for antivertigo preparations (adjusted OR [aOR] 2.48; 95% CI 1.92-3.19), anti-epileptics (aOR 2.34; 1.90-2.90), glucocorticoids (aOR 1.76; 1.52-2.03), urinary anti-spasmodics (aOR 1.72; 1.20-2.46), and muscle relaxants (aOR 1.33; 1.13-1.56). CONCLUSIONS: We observed markedly higher use of specific medications in MS cases in the 5 years before the first demyelinating claim. The overrepresentation of specific medications in MS cases, which was not fully explained by the physician diagnoses, may represent a signature of the MS prodrome.
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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 | 0.001 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".