Five years before multiple sclerosis onset: Phenotyping the prodrome
Bibliographic record
Abstract
Background: The multiple sclerosis (MS) prodrome is poorly characterized. Objective: To phenotype the MS prodrome via health care encounters. Methods: Using data from a population-based cohort study linking administrative and clinical data in four Canadian provinces, we compared physician and hospital encounters and prescriptions filled (via International Classification of Diseases chapters, physician specialty or drug classes) for MS subjects in the 5 years before the first demyelinating claim in an administrative cohort or the clinical symptom onset in an MS clinic-derived cohort, to age-, sex- and geographically matched controls. Rate ratios (RRs), 95% confidence intervals (95% CIs) and proportions were estimated. Results: The administrative and clinical cohorts included 13,951/66,940 and 3202/16,006 people with and without MS (cases/controls). Compared to controls, in the 5 years before the first demyelinating claim or symptom onset, cases had more physician and hospital encounters for the nervous (RR (range) = 2.31; 95% CI: 1.05–5.10 to 4.75; 95% CI: 3.11–7.25), sensory (RR (range) = 1.40; 95% CI: 1.34–1.46 to 2.28; 95% CI: 1.72–3.02), musculoskeletal (RR (range) = 1.19; 95% CI: 1.07–1.33 to 1.70; 95% CI: 1.57–1.85) and genito-urinary systems (RR (range) = 1.17; 95% CI: 1.05–1.30 to 1.59; 95% CI: 1.48–1.70). Cases had more psychiatrist and urologist encounters (RR (range) = 1.48; 95% CI: 1.36–1.62 to 1.80; 95% CI: 1.61–2.01), and higher proportions of musculoskeletal, genito-urinary or hormonal-related prescriptions (1.1–1.5 times higher, all p < 0.02). However, cases had fewer pregnancy-related encounters than controls (RR = 0.78; 95% CI: 0.71–0.86 to 0.88; 95% CI: 0.84–0.92). Conclusion: Phenotyping the prodrome 5 years before clinical recognition of MS is feasible.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".