Post-acute COVID-19 symptom risk in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Post-acute coronavirus disease 2019 (COVID-19) symptoms occurred in most of the COVID-19 survivors. However, few studies have examined the issue of whether hospitalization results in different post-acute COVID-19 symptom risks. This study aimed to compare potential COVID-19 long-term effects in hospitalized and non-hospitalized COVID-19 survivors. Methods This study is designed as a systematic review and meta-analysis of observational studies. A systematic search of six databases was performed for identifying articles published from inception until April 20th, 2022, which compared post-acute COVID-19 symptom risk in hospitalized and non-hospitalized COVID-19 survivors using a predesigned search strategy included terms for SARS-CoV-2 (eg, COVID, coronavirus, and 2019-nCoV), post-acute COVID-19 Syndrome (eg, post-COVID, post COVID conditions, chronic COVID symptom, long COVID, long COVID symptom, long-haul COVID, COVID sequelae, convalescence, and persistent COVID symptom), and hospitalization (hospitalized, in hospital, and home-isolated). The present meta-analysis was conducted according to The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement using R software 4.1.3 to create forest plots. Q statistics and the I2 index were used to evaluate heterogeneity in this meta-analysis. Results Six observational studies conducted in Spain, Austria, Switzerland, Canada, and the USA involving 419 hospitalized and 742 non-hospitalized COVID-19 survivors were included. The number of COVID-19 survivors in included studies ranged from 63 to 431, and follow-up data were collected through visits in four studies and another two used an electronic questionnaire, visit and telephone, respectively. Significant increase in the risks of long dyspnea (OR = 3.18, 95% CI = 1.90–5.32), anxiety (OR = 3.09, 95% CI = 1.47–6.47), myalgia (OR = 2.33, 95% CI = 1.02–5.33), and hair loss (OR = 2.76, 95% CI = 1.07–7.12) risk were found in hospitalized COVID-19 survivors compared with outpatients. Conversely, persisting ageusia risk was significantly reduced in hospitalized COVID-19 survivors than in non-hospitalized patients. Conclusion The findings suggested that special attention and patient-centered rehabilitation service based on a needs survey should be provided for hospitalized COVID-19 survivors who experienced high post-acute COVID-19 symptoms risk.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".