Predicting time to serologic diagnosis of AQP4+ NMOSD based on clinical factors and social determinants of health
Bibliographic record
Abstract
BACKGROUND: Early serologic diagnosis and initiation of targeted therapy are associated with better outcomes in aquaporin-4 IgG positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD). OBJECTIVE: To determine predictors of time to serologic diagnosis of AQP4+ NMOSD. METHODS: In CANOPTICS, a multi-centre, Canadian cohort study of NMOSD, we retrospectively evaluated time from the first clinical attack to first positive AQP4-IgG serology. We used a multivariable negative binomial regression model to evaluate possible predictors of time to diagnosis. RESULTS: We identified 129 participants with AQP4+ NMOSD from 7 centres. Diagnostic delay of >1 month was observed in 82 (63.6 %). Asian compared to European (White) ethnicity (IRR:0.40, 95 % CI:0.21-0.78), female sex (IRR:0.56, 95 % CI:0.32-0.99), later calendar year (IRR:0.84, 95 % CI:0.81-0.86), and hospitalization for the first attack (IRR:0.35, 95 % CI:0.20-0.62) were associated with shorter times to serologic diagnosis. We did not observe any overall effect of Afro-Caribbean ethnicity, but in exploratory analyses, Afro-Caribbean individuals with low income had longer times to diagnosis. CONCLUSION: More than 60 % of patients with NMOSD experienced delays to AQP4-IgG serologic diagnosis in this cohort. Given evidence of more adverse long-term outcomes in Afro-Caribbean individuals with NMOSD, intersectional effects of ethnicity and social determinants of health merit further study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".