Short Report: Prevalence of Cognitive Impairment in Newly Diagnosed Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is common in multiple sclerosis (MS) and can manifest early in the disease process, sometimes as early as the first demyelinating event. However, the frequency of cognitive impairment in a newly diagnosed MS population has not been evaluated comprehensively in a clinical population. We sought to examine the prevalence of cognitive impairment in relapsing-remitting MS (RRMS) within a year of diagnosis in a clinic where cognitive testing at diagnosis is part of routine practice. METHODS: A retrospective medical record review of persons with RRMS assessed in a cognitive MS clinic identified 107 patients assessed by the Minimal Assessment of Cognitive Function in Multiple Sclerosis battery within 1 year of a confirmed RRMS diagnosis. RESULTS: The cohort was predominantly female (n = 82 [76.6%]) and white (n = 93 [86.9%]). Only 36 patients (33.6%) were diagnosed as having RRMS based on a second clinical event. Processing speed was the most frequently impaired domain (n = 38 [35.5%]). Only 37 patients (34.6%) were within normal limits on all cognitive domains. Regarding mood symptoms, 25 patients (23.4%) were positive for depressive symptoms; 59 (55.1%), for anxiety. Severe fatigue was correlated with a lower score on the Symbol Digit Modalities Test (SDMT) (r = -0.380, P < .001), and higher depressive scores were correlated with lower performance on the SDMT (r = -0.397, P < .001) and the Paced Auditory Serial Addition Test (r = -0.254, P = .009). CONCLUSIONS: Cognitive impairment, specifically processing speed, and mood symptoms are frequently present in persons with newly diagnosed RRMS.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".