Detecting Cognitive Dysfunction in Multiple Sclerosis with a Magnetic Resonance Imaging Rating Scale: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: In multiple sclerosis (MS), magnetic resonance imaging (MRI) predictors of cognitive impairment are based on sophisticated computer-generated analyses that are difficult to apply in clinical settings. This study investigated the clinical usefulness of a new visual rating scale, the Cholinergic Pathways Hyperintensities Scale (CHIPS), in detecting cognitive dysfunction. METHODS: Forty clinically definite MS patients underwent a brain MRI. Based on the CHIPS, cholinergic pathway hyperintensities were rated in 10 regions on four axial slices. Computerized hyperintense lesion volumes were also obtained. For cognitive testing, The Neuropsychological Screening Battery for Multiple Sclerosis was used. "Low" and "High" lesion score groups were computed based on the mean of the total CHIPS score. Optimal sensitivity and specificity of the total CHIPS score in detecting cognitive impairment were determined using a receiver operator characteristic curve. RESULTS: Despite a similar demographic profile, subjects with a "High" lesion score performed significantly worse than the "Low" lesion score group on verbal (P = .007) and visuospatial (P = .02) memory, and on a global index of cognitive functioning (P = .001). Optimal sensitivity (82%) and specificity (83%) were reached with a threshold total CHIPS score of 18 points. Total CHIPS score and total hyperintense lesion load were correlated (sigma = 0.82, P < .0001). CONCLUSION: CHIPS is helpful in clinically predicting cognitive impairment 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".