Informative patterns of health care utilization preceding the recognition of adult-onset multiple sclerosis: A population-based study
Bibliographic record
Abstract
BACKGROUND: Early recognition of multiple sclerosis (MS) remains a pivotal challenge. Little is understood about the trajectories of health care use before recognition of adult-onset MS, and the relationship of the trajectories with subsequent disability. METHODS: We accessed linked clinical and population-based health administrative data in British Columbia, Canada (1991-2020). Using group-based multi-trajectory models, we described the joint trajectories of physician visits, hospitalizations, and prescription classes filled in the 10 years preceding MS recognition - either the first recorded demyelinating event (administrative index date, n = 6349) or MS symptom onset (clinical index date, n = 725) at age ≥ 18 years. Across the identified trajectories, we compared demographics and encounters potentially related to MS pre-index-date. In the clinical cohort, we assessed the relationship between the trajectories and disability scores using a linear mixed-effects model, adjusting for confounders. RESULTS: Before the administrative index date, we identified 3 trajectories: low (57 % of cohort), moderate (36 %), and high (6 %) health care use. Before the clinical index date, we also identified low (51 %), moderate (40 %), and high (9 %) trajectories. In both cohorts, individuals in the moderate and high (versus low) trajectories were more likely to be female and older, and have earlier neurologist and ophthalmologist visits, and nervous system diagnoses pre-index-date. The moderate (versus low) trajectory was associated with higher subsequent disability (adjusted beta coefficient=0.31;95 %CI:0.09-0.53). CONCLUSIONS: Earlier detection of MS by general practitioners and prompt MS drug treatment may be possible for patients accessing health care frequently, potentially mitigating disability progression.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".