Performance on Paced Auditory Serial Addition Test and cerebral blood flow in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: To assess the relationship between performance on the Paced Auditory Serial Addition Test (PASAT) and both cerebral blood flow (CBF) and axonal metabolic integrity in normal appearing white matter (NAWM) of the centrum semiovale in patients with multiple sclerosis (MS). METHODS: Normal appearing white matter of the centrum semiovale was investigated with magnetic resonance (MR) imaging in 28 non-depressed individuals (18 patients with MS and 10 healthy controls). CBF was assessed with pseudo-continuous arterial spin labeling. N-acetylacetate/creatine (NAA/Cr) ratios (a metabolic axonal marker) were measured using (1) H-MR spectroscopy. CBF was also measured in frontoparietal cortices and cerebellar hemispheres. RESULTS: In subjects with MS, we found a positive correlation between performance on the PASAT and CBF to the left centrum semiovale (P = 0.008), but not with the NAA/Cr ratio. There were no correlations between PASAT scores and CBF to the right centrum semiovale, frontoparietal cortices, and cerebellar hemispheres. There was no correlation between PASAT scores and NAA/Cr ratios. CONCLUSIONS: Our preliminary results suggest that performance on the PASAT in subjects with MS correlates with CBF to the left centrum semiovale, which contains left frontoparietal white matter association tracts involved in information processing speed and working memory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".