Intra-individual Variability as a Measure of Information Processing Difficulties in Multiple Sclerosis
Bibliographic record
Abstract
Deficits in information processing speed are among the most commonly reported impairments in multiple sclerosis (MS) and are generally assessed by evaluating mean-level performance on time-limited tests. However, this approach to assessing performance ignores potential within-subject differences in MS patients that may be useful for characterizing cognitive difficulties in MS. An alternative method of measuring performance is by examining the degree of within-subject variability, termed intra-individual variability (IIV). Intra-individual variability provides information about the characteristics of a person's performance over time and may provide novel information about cognitive functioning in MS. This study examined IIV in performance on the Computerized Test of Information Processing (CTIP) using two within-subject variability methods: individual standard deviation and coefficient of variation. Eighteen females with relapsing-remitting MS and 18 healthy female controls completed the CTIP. Consistent with previous research, MS patients demonstrated slower overall mean performance on the CTIP compared with controls, with patients becoming increasingly slower than controls as cognitive demands increased across the tasks. Furthermore, MS patients demonstrated greater IIV as measured by individual standard deviations on all subtests of the CTIP, even with mean-level group differences as well as practice and learning effects controlled. These between-group differences were not found when the coefficient of variation, a more coarse measure of within-subject variability, was used. Intra-individual variability was also found to be a better predictor of neurologic status than mean-level performance. These results suggest that IIV may provide unique insight into cognitive functioning 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".