A study of prevalence and factors related to cognitive impairment in Thai patients with neuromyelitis optica
Bibliographic record
Abstract
Abstract Background Neuromyelitis Optica (NMO) is a demyelinating disease caused by an autoimmune process that attacks the central nervous system resulting in a neurological deficit. Previous studies showed cognitive problems in these patients, especially the decline in processing speed, episodic memory, executive function, and attention. We aimed to study the prevalence and the factors related to cognitive impairment in Thai patients with NMO. Method We recruited 83 participants diagnosed with NMO by the 2015 international consensus diagnostic criteria for NMO spectrum disorder who visited the Demyelinating Disease Clinic at Siriraj Hospital. Participants with the last clinical relapse within 3 months or those who could not perform cognitive tests due to disability were excluded. Cognitive functions were assessed by using the Montreal Cognitive Assessment – Thai version (MoCA‐T) and Frontal Assessment Battery (FAB). The subjective reports of any cognitive problem by interviewing participants and their informants were also collected. Demographic data, cognitive performance, and factors related to the MoCA‐T score were analyzed. Result The mean MoCA‐T score in patients with NMO was 22.96 ± 3.82, and the mean FAB score was 15.27 ± 2.47. The majority (54%) of them had cognitive impairment according to the cut‐off of the MoCA‐T score of less than 25. Participants with cognitive impairment had significantly less education than those without cognitive impairment (9.9 4.3 and 14.6 3.6 years, p < 0.001), and they used more time to complete the MoCA‐T (p < 0.001). The disease severity evaluated by the Expanded Disability Severity Scale was associated with the MoCA‐T score (r = ‐0.29, p = 0.008). Age, the annual relapse rate in the first five years, duration of disease, and Aquaporin‐4 antibody status were not associated with cognitive impairment (all p > 0.5). Interestingly, the informant‐report of cognitive decline in NMO patients was associated with the less MoCA‐T performance (p = 0.032), but not the self‐report (p = 0.397). Conclusion Most of the Thai patients with NMO had cognitive impairment, which related to education, disease severity, and also the perception of their relatives. Future studies that included more comprehensive neuropsychological evaluations and neuroimaging findings are needed in these patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".