A national case–control study investigating demographic and environmental factors associated with NMOSD
Bibliographic record
Abstract
BACKGROUND: Risk factors for aquaporin-4 (AQP4+) antibody neuromyelitis optica spectrum disorder (NMOSD) are not well-established. OBJECTIVE: To investigate demographic and environmental factors associated with NMOSD using a validated questionnaire and case-control design. METHODS: We enrolled patients with AQP4 + NMOSD through six Canadian Multiple Sclerosis Clinics. Participants completed the validated Environmental Risk Factors in Multiple Sclerosis Study (EnvIMS) questionnaire. Their responses were compared to those of 956 unaffected controls from the Canadian arm of EnvIMS. We calculated odds ratios (ORs) for the association between each variable and NMOSD using logistic regression and Firth's procedure for rare events. RESULTS: In 122 participants (87.7% female) with NMOSD, odds of NMOSD in East Asian and Black participants were ⩾8 times that observed in White participants. Birthplace outside Canada was associated with an increased risk of NMOSD (OR = 5.5, 95% confidence interval (CI) = 3.6-8.3) as were concomitant autoimmune diseases (OR = 2.7, 95% CI = 1.4-5.0). No association was observed with reproductive history or age at menarche. CONCLUSION: In this case-control study, risk of NMOSD in East Asian and Black versus White individuals was greater than that observed in many previous studies. Despite the preponderance of affected women, we did not observe any association with hormonal factors such as reproductive history or age at menarche.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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, 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".