Symptom persistence and biomarkers in post-COVID-19/chronic fatigue syndrome – results from a prospective observational cohort
Bibliographic record
Abstract
Abstract Introduction Post-COVID-19 syndrome (PCS) is characterized by a wide range of symptoms, predominantly fatigue and exertional intolerance. While disease courses during the first year post infection have been repeatedly described, little is known about long-term health consequences. Methods We assessed symptom severity and various biomarkers at three time points post infection (3-8 months (mo), 9-16mo, 17-20mo) in 106 PCS patients with moderate to severe fatigue and exertional intolerance. A subset of patients fulfilled diagnostic criteria of myalgic encephalomyelitis/chronic fatigue syndrome (PCS-ME/CFS) based on the Canadian Consensus Criteria. Results While PCS-ME/CFS patients showed persisting symptom severity and disability up to 20mo post infection, PCS patients reported an overall health improvement. Inflammatory biomarkers equally decreased in both groups. Lower hand grip force at onset correlated with symptom persistence especially in PCS-ME/CFS. Discussion Debilitating PCS may persist beyond 20mo post infection, particularly in patients fulfilling diagnostic criteria for ME/CFS.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".