Post-COVID-19 fatigue: the contribution of cognitive and neuropsychiatric symptoms
Bibliographic record
Abstract
Fatigue in its many forms of physical, mental, and psychosocial exhaustion is a common symptom of post-COVID-19 condition, also known as "Long COVID." Persistent fatigue in COVID-19 patients is frequently accompanied by cognitive dysfunction and neuropsychiatric symptoms; however, less is known about the relationships between these components of post-COVID-19 condition and fatigue itself. Consequently, the present study sought to (1) distinguish the types of fatigue experienced by participants, and (2) investigate whether cognitive deficits across various domains and neuropsychiatric conditions predicted these different types of fatigue. The study included 136 COVID-19 patients referred for neuropsychological evaluation due to cognitive complaints 8 months on average after SARS-CoV-2 infection. Measures included self-reported fatigue (physical, cognitive, and psychosocial), neuropsychiatric questionnaires (assessing symptoms of depression, anxiety, apathy, and executive functioning), a comprehensive neuropsychological assessment, and self-reported quality of life and everyday functioning. Results showed that reports of clinical significant fatigue were pervasive in our sample (82.3% of participants), with physical fatigue rated highest on average relative to the subscale maximum. Elevated levels of apathy, anxiety, and executive dysfunction in neuropsychiatric measures along with executive and attentional difficulties on cognitive tests were found to be consistently important predictors among different types of fatigue. This implicates both cognitive and neuropsychiatric symptoms as predictors of fatigue in post-COVID-19 condition, and stresses the importance of a holistic approach in assessing and considering potential treatment for COVID-19 patients experiencing fatigue.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".