Distraction adds to the cognitive burden in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction in multiple sclerosis (MS) causes numerous limitations in activities of daily living. OBJECTIVES: To develop an improved method of cognitive assessment in people with MS using novel real-world distracters. METHODS: A sample of 99 people with MS and 55 demographically matched healthy controls underwent testing with the Minimal Assessment of Cognitive Functioning in Multiple Sclerosis (MACFIMS) and a modified version of the computerized Symbol Digit Modalities Test (c-SDMT). Half of the subjects completed the c-SDMT with built-in real-world distracters and half without. RESULTS: The mean time on the c-SDMT was significantly greater in MS subjects than healthy controls for both distracter ( p = 0.001) and non-distracter ( p < 0.001) versions. Significantly more MS subjects were impaired on the c-SDMT with distracters than the traditional SDMT (47.1% vs 30.3%, p = 0.04). There were no differences in impairment between the c-SDMT with and without distracters (47.1% vs 37.5%, p = 0.34). The distracter version had a sensitivity of 81% and specificity of 88% in detecting global cognitive impairment. CONCLUSIONS: The incorporation of distracters improves the sensitivity of a validated computerized version of the SDMT relative to the non-distracter and traditional versions and offers a quick and easy means of detecting cognitive impairment in people with 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".