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 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".