The relationship between cognitive function and neuropsychiatric disorders with quantitative electroencephalogram (qEEG) on long COVID syndrome patients
Bibliographic record
Abstract
Background: The COVID-19 pandemic has resulted in long-term consequences for a subset of affected individuals, known as long COVID syndrome. The neurological and psychiatric effects of this condition remain incompletely understood. This study aims to evaluate heightened common mental disorders in long COVID through assessing psychiatric, cognitive, neurophysiological aspects, and emphasizing lasting mental health impacts. Methods: This cross-sectional study compared patients with long COVID to those who had recovered from COVID-19 without residual symptoms using quantitative electroencephalogram (qEEG) analysis. We conducted qEEG analyses, and Montreal Cognitive Assessment (MoCA) and Self-Rating Questionnaire (SRQ) tests on participants. Analyses included brain spectrum examination, hemispheric asymmetry, and inter-electrode connectivity. Results: test). Conclusion: Our findings underscore the persistent neuropsychiatric impact of COVID-19, particularly in long COVID patients. Notably, working memory deficits in MoCA scores were identified as one of the most frequent neuropsychological symptoms in these individuals. Decreased brain connectivity indicates cognitive-sensorimotor decline and is confirmed by the frequent brain fog symptoms in long COVID.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".