P300 event-related potentials in patients with multiple sclerosis
Bibliographic record
Abstract
Abstract Background Cognitive impairment (CI) is a common and disabling symptom during the disease even in the earliest “preclinical” phase of patients with MS (pwMS). This study aims to assess cognitive function by measuring P300 event-related potential (ERP) and to look into the relationship between P300 abnormalities with the severity of the physical disability, education level, and disease duration. Methods Fifty pwMS (28 females and 22 males) aged 20–54 years and fifty healthy subjects comprised of 21 females and 29 males aged 18–50 years serves as the control group was studied. All participants underwent medical history, neurological examination, cognitive functions using the Montreal Cognitive Assessment scale (MoCA) and the P300 ERP. Results In this study, 48% of pwMS had CI. They had a longer P300 latency and a lower amplitude. Those with impaired cognition had a longer duration of illness and higher Expanded Disability Status Scale (EDSS), whereas those with intact cognition had a higher education level. P300 latency was correlated positively with EDSS and disease duration, but negatively with education level. P300 amplitude was found to be negatively correlated with EDSS, and disease duration but positively to the education level. Conclusions P300, as a non-invasive test, would support the presence of CI in pwMS patients and could be used for screening in daily practice. P300 has a strong relationship with illness duration, disease subtypes, EDSS, and education level.
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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 | 0.001 |
| 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) | 0.002 | 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".