Prevalence and significance of neurocognitive dysfunction in hepatitis C in the absence of correlated risk factors†
Bibliographic record
Abstract
Neurocognitive morbidity has been reported in individuals with chronic hepatitis C virus (HCV) infection, but the magnitude of such dysfunction in the absence of disease-correlated factors known to affect the central nervous system (e.g., substance abuse, cirrhosis, depression, interferon treatment) and the impact of any such change on functioning is unclear. We investigated a cohort of individuals with HCV, all of whom were carefully screened to exclude relevant comorbidities, to elucidate virus-related changes in the brain using neuropsychological tests and magnetic resonance spectroscopy (MRS). A cohort of 37 patients with chronic HCV infection was culled from 300 consecutive patients presenting to a tertiary care liver clinic. A comparison group of healthy controls (n = 46) was also assessed. Of 10 neurocognitive measures evaluated, the HCV group showed marginally poorer learning efficiency compared with controls; only 13% of patients demonstrated a clinical level of impairment on this test (defined as 1.5 SD below the normative standard). Although patients reported greater levels of fatigue and symptoms of depression, these factors did not correlate with the degree of learning inefficiency. With respect to MRS, the HCV group demonstrated increased choline and reduced N-acetyl aspartate relative to controls in the central white matter. Indicators of liver disease severity did not correlate with either memory or MRS abnormalities. In conclusion, while our findings support an association between hepatitis C and indicators of central nervous system involvement in a cohort of patients carefully screened to eliminate other factors influencing neurocognitive integrity, the clinical significance of these effects is limited.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".