Event-related evoked potential versus clinical tests in assessment of subclinical cognitive impairment in chronic hepatitis C virus
Bibliographic record
Abstract
CONTEXT: Chronic infection by hepatitis C virus causes impairment in neurocognitive function in up to 50% of patients which may not be detected by clinical tests. AIM: Early detection of neurocognitive impairment in chronic hepatitis C patients and investigating the cognitive function in HCV patient by p300 and clinical test. MATERIALS AND METHODS: The study included 60 patients with chronic hepatitis C and 30 healthy controls. Participants were subjected to a biochemical, hematological assessment, mini-mental state examination, Montreal Cognitive Assessment, P300, polymerase chain reaction (PCR), and fibroscan made for hepatitis C patients. RESULTS: The digit span, attention, concentration, and memory were significantly lower in patients than controls. The delayed P300 peak latency and the reduction of its amplitude were significantly evident in patients with liver fibrosis than the controls and patients without fibrosis. These abnormalities were significantly higher with increasing the grade of fibrosis. All patients with cognitive impairment (reduced mini-mental state score) had abnormal P300-evoked responses. P300 could detect neurocognitive impairment in some patients with normal neurocognitive functions by clinical test. P300 had sensitivity (100%), specificity (59.26), positive predictive value (75%), negative predictive value (100%), and accuracy (81.67) in the detection of neurocognitive impairment in HCV patient. CONCLUSION: Patients with chronic hepatitis C infection had significant impairment in their cognitive functions. This impairment increases with the increase in grade of hepatic fibrosis. P300 can detect minimal and subclinical impairment of cognitive function at early stages of chronic hepatitis with accuracy (81.67). TRIAL REGISTRATION: PACTR on 19 march 2018 retrospectively. Identification number for the registry is PACTR201804003215168.
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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 | 0.003 |
| 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 | 0.001 | 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".