Heterogeneity in health care pathways preceding the classical recognition of adult-onset multiple sclerosis: A multichannel state sequence analysis
Bibliographic record
Abstract
INTRODUCTION: Higher healthcare use before recognition of adult-onset multiple sclerosis (MS) raises the possibility of earlier disease detection. OBJECTIVE: To describe common clinical pathways before a first recorded demyelinating event or MS symptom onset. METHODS: We applied multichannel state sequence analyses to generate typologies of clinical pathways using linked clinical and population-based health administrative data in British Columbia, Canada (1991-2020). We constructed sequences of care providers and diagnostic claims in each 3-month period over the 5 years preceding the first recorded demyelinating event (N = 10,617) or MS symptom onset (N = 1761). We used the dynamic hamming distance to determine the dissimilarity between sequences. We applied hierarchical cluster analysis with Hubert's C Index to group similar pathways. RESULTS: Before the first demyelinating event, 9 pathways emerged: pathways for low (25 % of cohort), moderate (25 %) and high (18 %) healthcare use; pathways representing steadily increasing (6 %) and decreasing (10 %) healthcare use over the 5 years; specialist-specific pathways for visits to neurologists/neurosurgeons (2 %), ophthalmologists (6 %) and psychiatrists (2 %), and a musculoskeletal diagnoses-related pathway (5 %). Pre-MS symptom onset, 5 pathways were identified: low (42 %), moderate (32 %) and high (20 %) healthcare use; visits to psychiatrists (2 %); and musculoskeletal diagnoses (4 %). CONCLUSION: Adults with a pattern of recurrent visits to a neurologist/neurosurgeon or ophthalmologist could be targeted for earlier MS detection.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".