Five Years of Long COVID Syndrome: An Updated Review on Cardiometabolic and Psychiatric Aspects
Bibliographic record
Abstract
Five years after the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, there is still a significant number of people who have survived COVID-19 but never fully recovered from the disease. They go through an odyssey of doctor visits and a multitude of diagnostic tests, which ultimately do not provide concrete correlations and answers to the question of how exactly long COVID (LC) affects both physical and mental health, and performance. Often, not even highly technical and highly specialized methods, such as cardiac magnetic resonance imaging (MRI), can provide further explanation. Various research efforts continue to investigate the causes, effects and possible treatments of LC, particularly its impact on cognition and mental health. Patients with LC may experience persistent symptoms, but new symptoms also occur. Based on available studies, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) does not only affect the pulmonary system, but nearly every major system and organ, from the brain and heart to the kidneys and immune system. What mechanisms could explain the persistent symptoms of LC and the inadequate recovery? How valuable is an early internal and neurological examination, particularly in the context of psychotherapy? In this review, we examined which factors could contribute to the persistence of LC symptoms and to what extent mitochondrial impairment by LC can explain the symptoms of LC.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".