Minimal Neuropsychological Assessment of MS Patients: A Consensus Approach
Bibliographic record
Abstract
Cognitive impairment is common in multiple sclerosis (MS), yet patients seen in MS clinics and neurologic practices are not routinely assessed neuropsychologically. In part, poor utilization of NP services may be attributed to a lack of consensus among neuropsychologists regarding the optimal approach for evaluating MS patients. An expert panel composed of neuropsychologists and psychologists from the United States, Canada, United Kingdom, and Australia was convened by the Consortium of MS Centers (CMSC) in April, 2001. Our objectives were to: (a) propose a minimal neuropsychological (NP) examination for clinical monitoring of MS patients and research, and (b) identify strategies for improving NP assessment of MS patients in the future. The panel reviewed pertinent literature on MS-related cognitive dysfunction, considered psychometric factors relevant to NP assessment, defined the purpose and optimal characteristics of a minimal NP examination in MS, and rated the psychometric and practical properties of 36 candidate NP measures based on available literature. A 90-minute NP battery, the Minimal Assessment of Cognitive Function in MS (MACFIMS), emerged from this discussion. The MACFIMS is composed of seven neuropsychological tests, covering five cognitive domains commonly impaired in MS (processing speed/working memory, learning and memory, executive function, visual-spatial processing, and word retrieval). It is supplemented by a measure of estimated premorbid cognitive ability. Recommendations for assessing other factors that may potentially confound interpretation of NP data (e.g., visual/sensory/motor impairment, fatigue, and depression) are offered, as well as strategies for improving NP assessment of MS patients in the future.
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 | 0.066 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".