Long COVID and neuropsychiatric manifestations (Review)
Bibliographic record
Abstract
There is accumulating evidence in the literature indicating that a number of patients with coronavirus disease 2019 (COVID-19) may experience a range of neuropsychiatric symptoms, persisting or even presenting following the resolution of acute COVID-19. Among the neuropsychiatric manifestations more frequently associated with 'long COVID' are depression, anxiety, post-traumatic stress disorder, sleep disturbances, fatigue and cognitive deficits, that can potentially be debilitating and negatively affect patients' wellbeing, albeit in the majority of cases symptoms tend to improve over time. Despite variations in results obtained from studies using different methodological approaches to define 'long COVID' syndrome, the most widely accepted factors associated with a higher risk of developing neuropsychiatric manifestations include the severity of foregoing COVID-19, the female sex, the presence of comorbidities, a history of mental health disease and an elevation in the levels of inflammatory markers, albeit further research is required to establish causal associations. To date, the pathophysiological mechanisms implicated in neuropsychiatric manifestations of 'long COVID' remain only partially elucidated, while the role of the indirect effects of the COVID-19 pandemic, such as social isolation and uncertainty concerning social, financial and health recovery post-COVID, have also been highlighted. Given the alarming effects of 'long-COVID', interdisciplinary cooperation for the early identification of patients who are at a high risk of persistent neuropsychiatric presentations, beyond COVID-19 recovery, is crucial to ensure that appropriate integrated physical and mental health support is provided, with the aim of mitigating the risks of long-term disability at a societal and individual 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".