Persistence of post-COVID symptoms in the general population two years after SARS-CoV-2 infection: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective This meta-analysis investigated the prevalence of post-COVID symptoms two-years after SARS-CoV-2 infection. Methods Electronic literature searches on PubMed, MEDLINE, CINAHL, EMBASE, Web of Science databases, and on medRxiv/bioRxiv preprint servers were conducted up to October 1 st , 2023. Studies reporting data on post-COVID symptoms at two-years after infection were included. Methodological quality was assessed using the Newcastle-Ottawa Scale. Random-effects models were used for meta-analytical pooled prevalence of each symptom. Results From 742 studies identified, twelve met inclusion criteria. The sample included 7,912 COVID-19 survivors (50.7% female; age:59.5, SD:16.3). Post-COVID symptoms were assessed at a follow-up of 722.9 (SD:51.5) days after. The overall methodological quality of studies was moderate (mean: 6/10, SD:1.2 points). The most prevalent post-COVID symptoms two-years after SARS-CoV-2 infection were fatigue (28.0%, 95%CI 12.0-47.0), cognitive impairments (27.6%, 95%CI 12.6-45.8), and pain (8.4%, 95%CI 4.9-12.8). Psychological disturbances such as anxiety (13.4%, 95%CI 6.3-22.5) and depressive (18.0%, 95%CI 4.8-36.7) levels as well as sleep problems (20.9%, 95%CI 5.25-43.25) were also prevalent. Pooled data showed high heterogeneity (I 2 ≥75%). Conclusion This meta-analysis shows the presence of post-COVID symptoms in 30% of patients two-years after COVID-19. Fatigue, cognitive disorders, and pain were the most prevalent post-COVID symptoms. Psychological disturbances as well as sleep problems were still present two-years after COVID-19.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.056 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".