Perfusion reduction in the absence of structural differences in cognitively impaired versus unimpaired RRMS patients
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment affects 40%-68% of relapsing-remitting multiple sclerosis (RRMS) patients. Gray matter (GM) demyelination is complicit in cognitive impairment, yet cortical lesions are challenging to image clinically. We wanted to determine whether cortical cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT) differences exist between cognitively impaired (CI) and unimpaired (NI) RRMS. METHODS: Prospective study of healthy controls (n = 19), CI (n = 20), and NI (n = 19) undergoing magnetic resonance imaging (MRI) and cognitive testing <1 week apart. White matter (WM) T2 hyperintense lesions and T1 black holes were traced. General linear regression assessed the relationship between lobar WM volume and cortical and WM CBF, CBV, and MTT. Relationship between global and lobar cortical CBF, CBV, and MTT and cognitive impairment was tested using a generalized linear model. Adjusted Bonferroni p < 0.005 was considered significant. RESULTS: No significant differences for age, gender, disease duration, and any fractional brain or lesion volume were demonstrated for RRMS subgroups. Expanded Disability Status Scale (EDSS) and Hospital Anxiety and Depression Scale-Depression (HADS-D) were higher in CI. Lobar cortical CBF and CBV were associated with cognitive impairment (p < 0.0001) after controlling for confounders. Cortical CBV accounted for 7.2% of cognitive impairment increasing to 8.7% with cortical CBF (p = 0.06), while WM and cortical CBF accounted for 8.2% of variance (p = 0.04). CONCLUSION: Significant cortical CBF and CBV reduction was present in CI compared to NI in the absence of structural differences.
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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 | 0.001 |
| 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".