Cognitive impairment and magnetic resonance imaging correlations in Wilson's disease
Bibliographic record
Abstract
OBJECTIVES: To evaluate the cognitive performance of a group of patients with Wilson's disease (WD) and to correlate the cognitive findings with changes in magnetic resonance imaging (MRI). METHODS: All patients with WD consecutively attended in a Movement Disorders Clinic between September 2006 and October 2007 were invited to participate in the study, together with a group of matched healthy controls. Patients and controls were submitted to comprehensive neuropsychological assessment. MRI was performed in all patients, and abnormalities (high-intensity signal, low-intensity signal and atrophy) were semi-quantitatively rated. Performance of patients and controls in each cognitive test was compared, and correlations between cognitive scores and MRI changes were investigated within the patients' group. RESULTS: Twenty patients with WD (11 men) and 20 controls (nine men) were evaluated. Mean age in the WD and control groups was 30.05 ± 7.25 and 32.15 ± 5.37 years, respectively. Mean schooling years were 11.15 ± 3.73 among WD cases and 10.08 ± 2.62 among controls. Patients with WD performed significantly worse than controls in the Mini-Mental State Examination, Dementia Rating Scale, phonemic verbal fluency (FAS), verb generation, digit span forward, Stroop test, Frontal Assessment Battery and in the Brief Cognitive Screening Battery. A significant correlation emerged between global cognitive impairment and MRI scale (r = 0.535), being higher for high-intensity signal plus atrophy (r = 0.718). CONCLUSION: Patients with WD presented cognitive impairment, especially in executive functions, with good correlation between cognitive abnormalities and MRI changes.
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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) | 0.000 | 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 | 0.000 | 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".