Persistence of post-COVID symptoms in the general population two years after SARS-CoV-2 infection: A systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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, 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 7912 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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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 it