Cerebral haemodynamic changes accompanying cognitive impairment in primary lateral sclerosis
Bibliographic record
Abstract
Our objective was to elucidate the relationship between cognitive decline and cerebral haemodynamics in patients with PLS. We examined 18 patients with PLS and contrasted both neuropsychological and cerebral perfusion findings with seven age- and education-matched non-PLS controls. PLS patients were stratified into two groups based on the number of abnormal neuropsychological test scores: 1) cognitively intact PLS patients (PLS; those having zero or one abnormal scores (n =14)), and 2) cognitively-impaired PLS patients (PLSci; those having two or more abnormal test scores (n =4)). There was considerable heterogeneity in level of cognitive functioning with four patients meeting the criteria for cognitive impairment. The findings were highly consistent with a frontotemporal lobar dysfunction. Using CT perfusion to assess cerebral haemodynamics, the PLSci group had increased cerebral blood volume (CBV) and mean transit time (MTT) with reduced cerebral blood flow (CBF). More specifically, MTT was significantly increased (p<0.05) in the PLSci group compared with controls in all regions and affected both grey and white matter, with the exception of the temporal lobe and subcortical parietal white matter. These observations suggest that a subset of PLS patients is subject to cognitive decline and that this process is associated with changes in cerebral haemodynamics.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".