INTRA-INDIVIDUAL VARIABILITY IS AN IMPORTANT CHARACTERISTIC OF COGNITIVE FUNCTIONING IN PERSONS WITH MULTIPLE SCLEROSIS
Bibliographic record
Abstract
Cognitive deficits are highly prevalent in multiple sclerosis (MS) and have a negative\nimpact on daily life. Impairments in information processing speed are among the most\ncommonly reported deficits in MS and are generally assessed by evaluating mean-level\nperformance on time-limited tests. However, this approach to assessing performance\nignores potential within-subject differences that may be useful for characterizing\ncognitive difficulties in MS. An alternative method of measuring performance on timed\ncognitive tasks is to examine the degree of within-subject variability, termed intraindividual\nvariability (IIV). IIV provides information about the characteristics of an\nindividualâs performance and may provide novel information about cognitive functioning\nin MS and other neurodegenerative disorders. The research presented in this dissertation\nexamined IIV in performance as an indicator of cognitive functioning in persons with MS\nand explored the relations of performance variability to measures of neuronal\nconnectivity derived from resting state functional magnetic resonance imaging (rsfMRI).\nIndividuals with MS were found to be both slower and more variable on tests of\ninformation processing speed and attention. This variability was observed even when\ncontrolling for sensorimotor confounds and other systematic variables that may influence\nvariability, such as practice and learning effects. IIV in performance was found to better\ndistinguish MS patients from matched groups of healthy control subjects when compared\nto common clinical measures of cognitive performance or average response speed. These\ndifferences in IIV were also found consistently across six monthly assessments in a group\nwith MS who remained clinically stable over this period. This stability in IIV suggests its\nfeasibility as a measure of changes in longitudinal cognitive or clinical status. Using\nrsfMRI, greater stability in performance (i.e., lower IIV) was associated with greater\nfunctional connectivity between frontal lobe regions (i.e., ventral medial prefrontal cortex\nand frontal pole) in persons with MS. This increased connectivity appears to represent\npotential compensatory processes within mildly affected MS individuals. Together the\nfindings demonstrate that IIV is an important characteristic of cognitive performance that\nmay provide new insights into the cognitive deficits present in MS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".