Assessing Cognitive Fatigue in Multiple Sclerosis: A Multidimensional Approach
Bibliographic record
Abstract
Cognitive fatigue (CF) presents a considerable challenge for individuals with multiple sclerosis (MS) often negatively impacting quality of life. CF can be defined as a decrease in, or inability to sustain, optimal task performance throughout the duration of a continuous cognitive task. The following dissertation presents three original research reports which evaluate CF in MS in three distinct ways using a multidimensional approach. The objective of this dissertation was to comprehensively evaluate and quantify this frequently misunderstood symptom of the disease. The first report examines four theoretical models of CF in MS which evaluate the interrelatedness of disease severity, fatigue, depression, and sleep quality in order to determine their predictive roles with regard to CF. The second report assesses CF longitudinally by examining whether or not the ability to perform optimally on a continuous cognitive task changes as the disease progresses across a three-year time interval. The final report objectively quantifies CF in MS by evaluating changes in global and regional cerebral blood flow during a task of sustained attention using arterial spin labeling perfusion fMRI. Results of all three reports are further discussed in terms of clinical and research implications. CF is a symptom of MS not readily apparent to outside observers but presents a very real burden for people with the disease that negatively impacts their ability to lead active and productive lives. These individuals may be discriminated against because CF has thus far been a largely unverifiable subjective experience. The totality of these three studies allows for a multidimensional quantification of CF. By providing objective support to the self-reports of individuals with MS, not only can they achieve much needed validation, but this can also lead to interventions that may provide further direct benefit to their health-related quality of life.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".