Long-Term Impact of COVID-19: A Systematic Review of the Literature and Meta-Analysis
Bibliographic record
Abstract
Background: The long-term impact of COVID-19 is still unknown. This study aimed to explore post COVID-19 effects on patients chest computed tomography (CT), lung function, respiratory symptoms, fatigue, functional capacity, health-related quality of life (HRQoL), and the ability to return to work beyond 3 months post infection. Methods: A systematic search was performed on PubMed, Web of Science, and Ovid MEDLINE on 22 May 2021, to identify studies that reported persistent effects of COVID-19 beyond 3 months follow-up. Data on the proportion of patients who had the outcome were collected and analyzed using a one-group meta-analysis. Results: Data were extracted from 24 articles that presented information on a total of 5323 adults, post-infection, between 3 to 6 months after symptom onset or hospital discharge. The pooled prevalence of CT abnormalities was 59% (95% CI 44–73, I2 = 96%), abnormal lung function was 39% (95% CI 24–55, I2 = 94%), fatigue was 38% (95% CI 27–49, I2 = 98%), dyspnea was 32% (95% CI 24–40, I2 = 98%), chest paint/tightness was 16% (95% CI 12–21, I2 = 94%), and cough was 13%, (95% CI 9–17, I2 = 94%). Decreased functional capacity and HRQoL were found in 36% (95% CI 22–49, I2 = 97%) and 52% (95% CI 33–71, I2 = 94%), respectively. On average, 8 out of 10 of the patients had returned to work or reported no work impairment. Conclusion: Post-COVID-19 patients may experience persistent respiratory symptoms, fatigue, decreased functional capacity and decreased quality of life up to 6 months after infection. Further studies are needed to establish the extent to which post-COVID-19 effects continue beyond 6 months, how they interact with each other, and to clarify their causes and their effective management.
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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) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".