Long-Term Consequences of COVID-19 at 6 Months and Above: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
We aimed to review the data available to evaluate the long-term consequences of coronavirus disease 2019 (COVID-19) at 6 months and above. We searched relevant observational cohort studies up to 9 February 2022 in Pubmed, Embase, and Web of Science. Random-effects inverse-variance models were used to evaluate the Pooled Prevalence (PP) and its 95% confidence interval (CI) of long-term consequences. The Newcastle−Ottawa quality assessment scale was used to assess the quality of the included cohort studies. A total of 40 studies involving 10,945 cases of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection were included. Of the patients, 63.87% had at least one consequence at the 6 month follow-up, which decreased to 58.89% at 12 months. The most common symptoms were fatigue or muscle weakness (PP 6−12 m = 54.21%, PP ≥ 12 m = 34.22%) and mild dyspnea (Modified Medical Research Council Dyspnea Scale, mMRC = 0, PP 6−12 m = 74.60%, PP ≥ 12 m = 80.64%). Abnormal computerized tomography (CT; PP 6−12 m = 55.68%, PP ≥ 12 m = 43.76%) and lung diffuse function impairment, i.e., a carbon monoxide diffusing capacity (DLCO) of < 80% were common (PP 6−12 m = 49.10%, PP ≥ 12 m = 31.80%). Anxiety and depression (PP 6−12 m = 33.49%, PP ≥ 12 m = 35.40%) and pain or discomfort (PP 6−12 m = 33.26%, PP ≥ 12 m = 35.31%) were the most common problems that affected patients’ quality of life. Our findings suggest a significant long-term impact on health and quality of life due to COVID-19, and as waves of ASRS-CoV-2 infections emerge, the long-term effects of COVID-19 will not only increase the difficulty of care for COVID-19 survivors and the setting of public health policy but also might lead to another public health crisis following the current pandemic, which would also increase the global long-term burden of disease.
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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.004 | 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".