A systematic review and meta-analysis of discharged COVID-19 patients retesting positive for RT-PCR
Bibliographic record
Abstract
Background: With the increased number of patients discharged after having COVID-19, more and more studies have reported cases whose retesting was positive (RP) during the convalescent period, which brings a new public health challenge to the world.Methods: We searched PubMed, Web of Science, The Cochrane Library, CNKI, WanFang and VIP from December 1, 2019 to December 31, 2020.The included studies were assessed using JBI critical appraisal tools and Newcastle-Ottawa Scale.The RP rate of discharge patients was analyzed by a meta-analysis.We adhered to PRISMA reporting guideline.Findings: We have included 117 studies with 2669 RP participants after discharge.The methodological quality of 66 case reports were low to high, 42 case series and 3 cohort study were moderate to high, 3 case-control studies were moderate and 3 cross-sectional studies were low to moderate.The clinical manifestations of most RP patients were mild or asymptomatic, and CT imaging and laboratory examinations were usually normal.The existing risk factors suggest that more attention should be paid to sever patients, elderly patients, and patients with co-morbidities.The summary RP rate was 12¢2% (95% CI 10¢6À13¢7) with high heterogeneity (I 2 = 85%).Interpretation: To date, the causes and risk factors of RP result in discharged patients are not fully understood.High-quality etiological and clinical studies are needed to investigate these issues to further help us to make strategies to control and prevent its occurrence.
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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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".