Long‐Term Sequelae of COVID‐19: A Systematic Review and Meta‐Analysis of Symptoms 3 Years Post‐SARS‐CoV‐2 Infection
Bibliographic record
Abstract
ABSTRACT The symptoms of long COVID are well‐documented. However, the long‐term effects beyond 2 years remain poorly understood due to a lack of data. This systematic review and meta‐analysis examined the prevalence of persistent symptoms in COVID‐19 survivors 3 years following initial SARS‐CoV‐2 infection. PubMed, MEDLINE (Ovid), CENTRAL, Web of Science, Scopus, and Embase were searched from inception of the databases up to July 20, 2024, by two independent researchers for articles reporting on the prevalence of persistent symptoms 3 years' post‐infection of people who survived COVID‐19 infection. We employed a random‐effect model for the pooled analysis, and the meta‐analytical effect size was prevalence for the applicable end‐points, I 2 statistics, and quality assessment of included studies using the Newcastle‐Ottawa Scale. Eleven articles were included after the literature search yielded 223 potentially relevant articles. We found that among patients with long COVID, fatigue, sleep disturbances, and dyspnea were the most common symptoms. Pooled analysis showed that the proportion of individuals experiencing at least one persistent symptom 3 years post‐COVID‐19 is 20% (95% confidence interval [CI]: 8–43). The prevalence of persistent symptoms was dyspnea (12%; 95% CI: 10–15), fatigue (11%; 95% CI: 6–20), insomnia (11%; 95% CI: 2–37), loss of smell (7%; 95% CI: 5–8), loss of taste (7%; 95% CI: 3–16), and anxiety (6%; 95% CI: 1–32). Prevalence of other findings include impaired diffusion capacity (42%; 95% CI: 34–50) and impaired forced expiratory volume in 1 s (10%; 95% CI: 8–12). Our findings confirm the persistence of unresolved symptoms 3 years post‐COVID‐19 infection, with implications for future research, healthcare policy, and patient care.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".