Detecting Subtle Cognitive Impairment in Multiple Sclerosis with the Montreal Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND: Although cognitive deficits are frequent in multiple sclerosis (MS), screening for them with tools such as the Montreal Cognitive Assessment (MoCA) test is usually not performed unless there is a subjective complaint. The Multiple Sclerosis Neuropsychological Questionnaire (MSNQ) is among the instruments most commonly used to assess self-reported subjective complaints in MS. Nonetheless, it does not always accurately reflect cognitive status; many patients with cognitive deficits thus fail to receive appropriate referral for detailed neuropsychological evaluation. The objective of this study was to examine the validity of the MoCA test to detect the presence of objective cognitive deficits among patients with MS without subjective complaints using the Minimal Assessment of Cognitive Function in MS (MACFIMS) as the gold standard. METHODS: The sample included 98 patients who were recruited from a university hospital MS clinic. The MSNQ was used to select patients without subjective cognitive complaints who also completed the MACFIMS, MoCA test and MSQOL-54. RESULTS: 23.5% of patients without subjective cognitive complaints had evidence of objective cognitive impairment on the MACFIMS (z score < -1.5 on two or more tests). The MoCA had a sensitivity of 87% and a specificity of 68% for detecting objective cognitive impairment in this patient population using a cut-off score of 27. CONCLUSION: A significant proportion of patients without self-reported cognitive impairment do have evidence of cognitive deficits on more exhaustive cognitive assessment. The MoCA is a rapid screening test that could be used to target patients for whom a more detailed neuropsychological assessment would be recommended.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".