Reasons for Healthcare Use Before Onset of Multiple Sclerosis: A Nationwide Matched Cohort Study in Sweden
Bibliographic record
Abstract
BACKGROUND: Evidence suggests signs and symptoms may emerge years before the clinical onset of multiple sclerosis (MS). We investigated secondary healthcare use and medication dispensations before MS onset. METHODS: Using linked administrative data, we analyzed a neurologist-diagnosed MS clinical cohort and an algorithm-defined administrative cohort in Sweden (2001-2019). People with MS (PwMS) were matched to up to five non-MS comparators by sex, birth year, residency location, and residency duration before the index date (clinical: symptom onset; administrative: first MS/demyelinating diagnosis code). Annual outpatient visits and hospitalizations by diagnosis codes across 19 years pre-index, and dispensed medications by anatomical and therapeutic classifications across 14 years pre-index were compared. RESULTS: The clinical cohort included 7604 PwMS and 37,974 matched comparators (median age at symptom onset = 35.5; 68.5% females). In the 5 years pre-index, outpatient visits were 11%-73% higher among PwMS for disturbances of sense organs, nervous, musculoskeletal, digestive, and genitourinary systems, mental health/behavior, and ill-defined symptoms/signs, with visits related to sense organs elevated up to 6 years. Hospitalizations were often elevated in the year pre-index. Dispensations of nervous system-related, musculoskeletal, blood-related, metabolic, sensory, respiratory, and dermatological agents were elevated by 6%-22% in the 5 years pre-index, with elevations in nervous system-related and musculoskeletal agents extending up to 6-9 years pre-index. Findings were similar in the administrative cohort with greater magnitudes and longer durations pre-index. CONCLUSIONS: We observed increased hospital, outpatient, and prescription utilization for multiple body systems 6-9 years pre-MS onset. These patterns provide a more comprehensive picture of the MS prodrome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".