Injury preceding the classical recognition of multiple sclerosis: A population-based study
Bibliographic record
Abstract
BACKGROUND: We investigated the association between multiple sclerosis (MS) and fractures, dislocations/sprains/strains, and burns preceding MS recognition. METHODS: We conducted a cohort study using clinical and population-based health administrative data in British Columbia, Canada (1991-2020). We compared the risk of a fracture, dislocation/sprain/strain, and burn in the six years preceding an MS cases' first demyelinating claim (administrative cohort=9197) or MS symptom onset (clinical cohort=1446) to that of matched general population controls using modified Poisson regression. As sensitivity analyses, we used high-dimensional propensity scores (hdPS) to address residual confounding and targeted maximum likelihood estimation (TMLE) for mis-specification. RESULTS: In the six years before the first demyelinating claim (administrative cohort), the risk of a fracture (adjusted relative risks [adjRR]=1.28;95 %CI:1.20-1.36), dislocation/sprain/strain (adjRR=1.20;95 %CI:1.15-1.23), and burn (adjRR=1.40;95 %CI:1.22-1.62) was higher among MS cases. After hdPS adjustment and TMLE, the adjusted relative risks decreased slightly: fracture (hdPS=1.20; TMLE=1.20), dislocation/sprain/strain (hdPS=1.15; TMLE=1.15), and burn (hdPS=1.25; TMLE=1.26). Pre-MS symptom onset (clinical cohort), the associations were weaker but in the same direction. CONCLUSION: Fractures, dislocations/sprains/strains, and burns were more common among people with MS before its classical recognition, suggesting that MS could be detected earlier.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".