Fatigue symptoms associated with COVID-19 in convalescent or recovered COVID-19 patients; a systematic review and meta-analysis
Bibliographic record
Abstract
BackgroundThe prevalence and prognosis of post-acute stage SARS-CoV-2 infection fatigue symptoms remain largely unknown. AimsWe performed a systematic review to evaluate the prevalence of fatigue in post-recovery from SARS-CoV-2 infection. MethodMedline, Embase, PsycINFO, CINAHL, Web of Science, Scopus, trial registries, Cochrane Central Register of Controlled Trials and Google Scholar were searched for studies on fatigue in samples that recovered from PCR diagnosed COVID-19. English, French and Spanish studies were included. Meta-analyses were conducted separately for each recruitment setting. Results We identified 41 studies with 9362 patients that recovered from COVID-19. Post-COVID-19 patients self-report of fatigue was higher compared to healthy controls (RR = 3.688, 95%CI [2.502, 5.436], p < 0.001). Over 50% of patients discharged from inpatient care reported symptoms of fatigue during the first (ER = 0.517, 95%CI [0.278, 0.749]) and second month following recovery (ER = 0.527, 95%CI [0.337, 0.709]). 10% of the community patients reported fatigue in the first-month post-recovery. Patient setting moderated the association between COVID-19 recovery and fatigue symptoms (R2 = 0.11, p < 0.001). Female patients recovering from COVID-19 had a greater self-report of fatigue (OR = 1.782, 95%CI [1.531, 2.870]). Patients recruited through social media had fatigue above 90% across multiple time points. Fatigue was highest in studies from Europe.ConclusionFatigue is a symptom associated with functional challenges which could have economic and social impacts. Developing long-term planning for fatigue management amongst patients beyond the acute stages of SARS-CoV-2 infection is essential to optimizing patient care and public health outcomes. Further studies should examine the impact of sociodemographic, pandemic-related restrictions and pre-existing conditions on fatigue.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| 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) | 0.003 | 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".