Cognition and Cognitive Fatigability: Association with Employment Status in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Slowed processing speed impacts employment status in people with multiple sclerosis (PwMS). Studies on the Multiple Sclerosis Functional Composite (MSFC), which includes the Paced Auditory Serial Addition Test (PASAT), have demonstrated that the combined score predicts employment status. Whether PASAT performance alone is associated with employment status is less clear. In addition, no studies have yet evaluated whether cognitive fatigability (CF), as measured with the PASAT, is associated with employment status. The aim of the current study was to examine the association between PASAT performance, CF, and employment status in PwMS. METHODS: Hundred and eighty-six PwMS completed the PASAT as part of a larger neuropsychological battery. ANOVAs and chi-squares analyzed group differences between employed and unemployed participants with respect to demographics, PASAT performance scores, and CF. Linear regression determined whether PASAT performance and/or CF scores were associated with employment status. RESULTS: After controlling for demographic influences, group differences were noted between employed vs. unemployed individuals on PASAT performance scores only. Employment status was associated with PASAT performance scores but not CF. CONCLUSIONS: The current study confirmed that PASAT performance is associated with employment status in MS. Given that CF was not associated, it seems difficulties with information processing speed (IPS) and working memory have more impact on a PwMS's ability to remain employed rather than within-task performance decline.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".