Self-assessment of Cognition in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: : To investigate the role of personality and anxiety to self-report measures of cognition in patients with multiple sclerosis (MS). BACKGROUND: : Self-report measures of cognition have consistently been shown to correlate better with depressed mood than neuropsychological test performance in patients with MS, with few studies focusing on the role of anxiety and personality. METHOD: : One hundred eight MS patients completed the following: (a) patient and informant report Multiple Sclerosis Neuropsychological Questionnaire (MSNQ); (b) Hospital Anxiety and Depression Scale; (c) cognitive assessment with the Brief Repeatable Battery of Neuropsychological Tests; and (d) personality assessment using the self-report NEO Five-Factor Inventory. RESULTS: : Higher patient MSNQ (P-MSNQ) scores (greater reported cognitive dysfunction) were significantly correlated with lower scores on the Paced Auditory Serial Addition Test (PASAT; r=-0.20, P<0.05), increased depression (r=0.45, P<0.01) and anxiety (r=0.54, P<0.01), higher neuroticism (r=0.51, P<0.01), and lower conscientiousness (r=-0.35, P<0.01). After controlling for demographic variables, significant predictors of P-MSNQ scores were anxiety (ΔR=0.272, P<0.001), conscientiousness (ΔR=0.067, P=0.002), and performance on the PASAT (ΔR=0.050, P=0.005). Depression and neuroticism did not contribute significant variance in comparison to anxiety. CONCLUSIONS: : Overall, patient self-reports of cognition did not correspond well to neuropsychological performance. Anxiety and conscientiousness contributed significantly to patients' perceptions of their cognitive failings and thus should be taken into account when addressing these complaints.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".