Bibliographic record
Abstract
Interest in neuromyelitis optica (NMO) has increased substantially over the last few years, but it is not known whether NMO has the same geographic and temporal variations in disease risk as multiple sclerosis (MS). We aimed to evaluate the worldwide incidence and prevalence of NMO through a systematic review of published peer-reviewed studies. We performed a search of the English-language literature using MEDLINE and EMBASE from January 1985 to March 2012. Search terms included "neuromyelitis optica," "Devic's," "opticospinal," "incidence," "prevalence," and "epidemiology." We assessed study quality using a standardized instrument. A total of five studies met the inclusion criteria. Three of the studies were from North America, and all studies were published between 2005 and 2012. All studies were of good quality, but only one study reported standardized rates, and subgroup-specific estimates were rarely reported. The incidence of NMO per 100,000 population ranged from 0.053 to 0.40, while the prevalence per 100,000 population ranged from 0.52 to 4.4. Heterogeneity was high among the incidence (I(2) = 68.0%) and prevalence studies (I(2) = 94.0%). This review highlights the limited knowledge regarding the epidemiology of NMO and the importance of obtaining estimates standardized to common populations to enhance comparability of studies from different jurisdictions. Future studies would also benefit from reporting age-, sex-, and race- or ethnicity-specific estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".