Psychiatric morbidity during the multiple sclerosis prodrome is associated with future disability
Bibliographic record
Abstract
BACKGROUND: Evidence suggests a prodromal phase in multiple sclerosis (MS) identifiable via healthcare use, including psychiatric symptoms. The association between psychiatric morbidity in this phase and future outcomes remains unclear. OBJECTIVES: We investigated the association between psychiatric morbidity in the 5-years pre-MS onset and subsequent disability (EDSS) scores. METHODS: We identified MS patients who visited an MS clinic in British Columbia, Canada (1991-2018) and linked their clinical and population-based health administrative data. Psychiatric morbidity was identified using physician/hospital visits in the 5-years pre-MS onset. Multivariable generalized linear models examined the association between psychiatric morbidity and subsequent EDSS scores. We explored effect modification by sex, age, and MS course and investigated if high psychiatric-related physician visits (>median) or hospitalizations (measures of "psychiatric morbidity burden") were associated with EDSS scores. RESULTS: Among 2212 MS patients, 481 (21.7%) had psychiatric morbidity in the 5-years pre-MS onset. Follow-up averaged 5.2 (SD: 4.9) years (first-to-last EDSS assessment). Psychiatric morbidity pre-MS onset was associated with higher post-diagnosis EDSS scores (covariate-adjusted[adj) β = 0.17; 95% confidence interval (CI): 0.03-0.30). Associations were more pronounced in males (adjβ = 0.43; 95% CI: 0.04-0.83), <30 years (adjβ = 0.44; 95% CI: 0.15-0.73), relapsing-onset-MS (adjβ = 0.22; 95% CI: 0.08-0.37) and high psychiatric-related physician visit burden (adjβ = 0.23; 95% CI: 0.05-0.41), or hospitalizations (adjβ = 0.48; 95% CI: 0.002-0.96). CONCLUSIONS: Psychiatric morbidity before MS recognition was associated with increased future disability, particularly in males, younger individuals, relapsing-onset-MS, and high pre-MS onset psychiatric morbidity burden.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".