Correlation of cortical lesions of multiple sclerosis at double inversion recovery with cognition screening scores
Bibliographic record
Abstract
Abstract Background Multiple sclerosis is a chronic inflammatory disease affecting both white and gray matters of the central nervous system. It has been approved that the degree of gray matter involvement is closely associated with the degree of physical disability and the extent of cognitive impairment. Thus, it is necessary to incorporate widely available simple methods for neurocognitive evaluation and gray matter detection in the periodic assessment of MS patients that will influence treatment decisions. Objectives To assess the correlation of cortical lesions of multiple sclerosis (MS) at double inversion recovery (DIR) with cognition screening scores Methods This study was conducted on 30 patients with MS with an average age of 31.3±13.6 years. All of them underwent MRI and clinical assessment with the calculation of Expanded Disability Status Scale (EDSS), Montreal Cognitive Assessment (MoCA), and Symbol Digit Modality Test (SDMT) scores. The image analysis was performed by 2 reviewers for cortical lesion number, shape, and subtypes, and total lesion load. Results Both MoCA and SDMT scales had a significant inverse correlation with cortical lesions number (r=− 0.68, − 0.72) respectively and total lesion load (r=− 0.53, − 0.65) respectively. Besides, there was a significant inverse correlation between the MoCA test, varied cortical subtypes: leukocortical, juxtacortical, and intracortical subtypes (r = − 0.63, − 0.56, − 0.52) respectively, and different cortical lesion shapes: oval, wedge, and curvilinear shaped (r = − 0.62, − 0.69, − 0.49) respectively. As well, the SDMT scale showed a significant inverse correlation with varied cortical subtypes: intracortical, leukocortical, and juxtacortical subtypes (r = − 0.63, − 0.61, − 0.57) respectively, and different cortical lesion shapes: oval, curvilinear, and wedge shaped (r = − 0.61, − 0.59, − 0.46) respectively. Interestingly, there was an excellent inter-observer correlation of cortical lesion number (r = 0.96), total lesion load (r = 0.95), subtypes of cortical lesion (r = 0.94), and cortical lesion shapes (r = 0.77). Conclusion We concluded that DIR can detect cortical lesions of MS, and MRI findings were well-correlated with cognitive dysfunction in these patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".