Post-COVID-19 Syndrome or Long COVID: From Patient Symptoms to Current Pathophysiological Hypotheses
Bibliographic record
Abstract
Mounting evidence supports the existence of significant sequelae of coronavirus disease 2019 (COVID-19), the so-called post-COVID-19 syndrome or long COVID, whose real incidence is unknown. Paradigmatic examples of this syndrome, whose pathogenesis and mechanisms are currently being investigated, emerge from outpatient consultations of persons who recovered from COVID-19. These patients are deeply involved psychologically and struggle to find the cause of their symptoms. They often look for other long COVID patients on the web, sharing diagnostic workup and therapeutic choices that, however, are not defined at present. There is no specific clinical definition of long-term COVID-19 agreed by the medical community. The clinical picture can be extremely variable, including non-specific symptoms, such as fatigue and low-grade fever, or symptoms that could be related to organ damage, such as cough, breathlessness, palpitations, joint pain and abdominal pain. Residual organ injury can be detected through routine investigations. Persistent symptoms following COVID-19 include symptoms related to chronic inflammation, symptoms related to organ damage and symptoms related to hospitalization and/or isolation. Having been affected by COVID-19 may have a profound impact on patients mental health. Moreover, persistent direct viral effects have been advocated as a possible cause of long COVID-19, including neuronal injury, post-viral post-traumatic stress disorder and mast cell activation syndrome. Also, several drugs are possibly involved in post-COVID-19 syndrome onset. At present, no defined treatment for this syndrome can be recommended. Its impact in terms of morbidity and late mortality has yet to be determined. Since studies have been limited by a relatively short follow-up of post-acute patients, well-designed, prospective, long-term follow-up studies are clearly warranted. Clin Infect Immun. 2021;6(2):34-39 doi: https://doi.org/10.14740/cii128
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".