Long-term symptom severity and clinical biomarkers in post-COVID-19/chronic fatigue syndrome: results from a prospective observational cohort
Bibliographic record
Abstract
Background: Post-COVID-19 syndrome (PCS) is characterised by a wide range of symptoms, primarily fatigue and exertion intolerance. While disease courses in the early months post-infection have been well-described, the long-term health consequences for patients with PCS with disabling fatigue remain unclear. Methods: In this prospective observational cohort study, we evaluated symptom severity and various biomarkers, including hand grip strength (HGS), cardiovascular function, and laboratory parameters, in 106 patients with PCS with moderate to severe fatigue and exertion intolerance at three time points after infection (3-8, 9-16, and 17-20 months). The study was conducted at the Charité's Fatigue Centre and the Charité's outpatient clinic for neuroimmunology at Berlin, Germany from July 16, 2020, to February 18, 2022. A subset of patients (PCS-ME/CFS) met the diagnostic criteria for myalgic encephalomyelitis/chronic fatigue syndrome according to the Canadian Consensus Criteria (CCC). The aim was to determine differences in the disease course between the two patient groups (i.e., PCS vs PCS-ME/CFS) and identify correlating biomarkers. Findings: Patients with PCS-ME/CFS reported persistently high severity of most symptoms up to 20 months after infection, while patients with PCS showed overall health improvement. Although fatigue and post-exertional malaise (PEM), hallmarks of post-infectious fatigue syndromes, were still evident in both groups, they remained more pronounced in PCS-ME/CFS. Inflammatory biomarkers decreased in both groups, but not antinuclear antibodies. Lower HGS at onset correlated with symptom persistence, particularly in patients with PCS-ME/CFS. Interpretation: Our findings suggest that PCS can persist beyond 20 months post-infection and encompass the full scope of post-infectious ME/CFS as defined by the CCC. Sub-classifying patients with PCS based on the CCC can assist in the management and monitoring of patients with PCS-ME/CFS due to their persistently higher symptom severity. Funding: C. S. was supported by a grant from the Weidenhammer-Zoebele Foundation. F. K. was supported by the Volkswagen Foundation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".