Prodrome in relapsing‐remitting and primary progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The multiple sclerosis prodrome remains poorly understood. We aimed to examine the prodrome in people with relapsing remitting multiple sclerosis at onset (RMS) and primary progressive multiple sclerosis (PPMS). METHODS: We conducted a matched cohort study using clinical and linked health administrative data in two Canadian provinces. We identified people with RMS, PPMS and age- sex- and geographically-matched population controls, and compared the number of physician encounters (total number, per International Classification of Diseases chapter, and per physician speciality) in the five years before symptom onset. Negative binomial regression models were sex, age, socioeconomic status and calendar year adjusted. RESULTS: We identified 1887 RMS, 171 PPMS cases, and 9837 matched population controls. No difference existed in the total number of encounters in the five years before index between RMS and PPMS, or between the phenotypes and their respective controls. Compared to RMS cases, PPMS cases had more nervous system-related encounters (adjusted rate ratio, 3.00; 95% confidence interval, 1.06-8.49) and fewer encounters with dermatologists (adjusted rate ratio 0.53; 95% confidence interval, 0.30-0.96). CONCLUSION: Findings suggest that people with RMS and PPMS may both experience a prodrome, although aspects may differ.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".