Epidemiology of NMOSD in Catalonia: Influence of the new 2015 criteria in incidence and prevalence estimates
Bibliographic record
Abstract
BACKGROUND: Population-based studies on neuromyelitis optica spectrum disorders (NMOSD) are limited, and it is unclear whether the rates have changed with the implementation of the new 2015 criteria. OBJECTIVES: To estimate the incidence and prevalence of NMOSD in Catalonia (Spain), using both the 2006 and the 2015 criteria. METHODS: In this clinic-based retrospective study, patients diagnosed with NMOSD between 2006 and 2015 were identified using multiple sources, including direct contact to all Catalan hospitals, identification of cases through the Catalan Health Surveillance System, and registry of antibodies to aquaporin-4 (AQP4-IgG) and myelin oligodendrocyte glycoprotein (MOG-IgG) in a reference laboratory. The incidence rate was calculated for the period 1 January 2006-1 January 2016 and prevalence for the date 1 January 2016. RESULTS: We identified 74 patients (by the 2015 criteria). Most patients were Caucasian (81%), and female (76%) with a median age at disease onset of 42 years (range, 10-76 years). In total, 54 (73%) patients were positive for AQP4-IgG, 11 (15%) double-seronegative, and 9 (12%) MOG-IgG-positive. Rates of incidence and prevalence (0.63/1,000,000 person-years and 0.89/100,000, respectively) were 1.5-fold higher than those reported by the 2006 criteria. Lowest rates were seen in children and elder people and highest in women and middle-aged people (40-59 years). The female predominance was lost in incident AQP4-IgG-seronegative children and AQP4-IgG-positive elder people. MOG-IgG and double-seronegativity contributed similarly but did not influence the long-term outcome. CONCLUSION: The new criteria increase the estimates, but NMOSD remains as a rare disease. The differences in age- and sex-specific estimates highlight the importance of the serologic classification.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".