The link between event-related potentials and cognitive dysfunction in Multiple Sclerosis: A systematic review
Bibliographic record
Abstract
Introduction: Multiple Sclerosis is one of the most frequent causes of Central Nervous System disease in young persons. It is associated with cognitive impairment in 70% of cases, resulting in a diminished quality of life. This systematic review aimed to evaluate if event-related potentials (ERPs) can be a relevant diagnosis tool of cognitive dysfunction in MS. Methods: Four databases were consulted (PubMed, Embase, Scielo and Web of Science) until March 10th, 2021. The research was made combining keywords, MESH or EMTREE terms. The inclusion criteria were 1) = 18 years old, 2) diagnosis according to specific MS criteria, 3) with or without cognitive complaints, 4) independently of the time of diagnosis and usual medication, and 5) articles written in Portuguese, Spanish, English and French. Papers should use ERPs, compared with normative values, Scales of Cognitive Evaluation and/or healthy control groups. The expected outcome was ERPs amplitude and/or latency variation (P300, N400 and mismatch negativity – MMN). Results: 425 articles were obtained initially, with 26 articles in the end. P300 was the most discussed ERP (25 articles), demonstrating a reduced amplitude or an increased latency in 84% of those. N400 was evaluated in one and MMN was addressed in two articles. ERPs were compared with Mini-Mental State Exam in two of them and with Montreal Cognitive Assessment in three. Other scales were also compared with ERPs in twenty articles. Conclusion: MS patients with cognitive impairment demonstrated overall ERP abnormalities, suggesting that ERPs may be an appropriate diagnostic method for cognitive impairment in MS.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".