The Montreal Cognitive Assessment (MoCA) in Multiple Sclerosis: Relation to Clinical Features
Bibliographic record
Abstract
Background: The Montreal Cognitive Assessment (MoCA) is quickly becoming the most common clinical screen for cognitive impairment. Cognitive impairment is a frequent symptom of multiple sclerosis (MS) and can be difficult to detect in routine evaluation. Although specific screening measures have been studied and established for use in MS, MS cognitive screening tools may not be implemented in a general neurology setting. Method: The present study sought to characterize the use of the Montreal Cognitive Assessment (MoCA) a brief measure employed in general neurologic practice for the detection of cognitive impairment in MS. The MoCA, along with other clinical measures, was administered to consecutively-recruited outpatients diagnosed with MS (N=259) to describe the findings and determine the frequency of detection of impairment. A subgroup (n=28) was also administered the oral version of the Symbol Digit Modalities Test (SDMT) as a measure of information processing speed. Results: Participants' mean age was 45.8 ± 13.3 years and were diagnosed with relapsing-remitting MS (66%), secondary-progressive MS (18%), primary progressive MS (10%), clinically isolated syndrome (6%) or radiologically isolated syndrome (<1%). Median EDSS score was 2.5. A total of 41% of the sample scored in the impaired MoCA range (cutoff score of 26) with a mean of 25.86 ±2.92 points. EDSS was the clinical variable that most strongly predicted MoCA score. The combined SDMT and MoCA score led to the strongest prediction of EDSS score. Conclusion: In an outpatient setting where MS-specific cognitive screening is not implemented, the MoCA can be a helpful to identify those MS patients who warrant full neuropsychological evaluation.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".