Liver injury in post-acute COVID-19 syndrome: A systematic review and meta-analysis of early observational studies
Bibliographic record
Abstract
Background: Post-acute COVID-19 syndrome (PACS; long COVID) is characterized by persistent or delayed symptoms at least 4 weeks from acute COVID-19 infection. Given the well-documented incidence of liver injury in acute COVID-19, this systematic review aims to assess the odds of liver injury in earlier experiencers of PACS. Methods: Observational studies published prior to March 2022 were screened for data describing liver injury (defined per primary study) in patients with PACS. Results: A total of 2,117 abstracts and 35 full texts were screened, of which 26 met the inclusion criteria. The mean time since acute COVID infection across all studies was 195.5 days. Seven studies included COVID-negative control groups. Twenty-three studies measured lab findings, and nine studies measured imaging or elastography. Five studies were eligible for meta-analysis of odds ratios, which did not demonstrate a statistically significant difference in odds for liver injury in patients with PACS compared with COVID-negative patients (OR 2.22 [95% CI 0.51–9.61; p = 0.28]). Newcastle-Ottawa Scale assessments for all studies found 24 of 26 studies with high to very high risk of bias. ROBINS-E assessments for studies included in the meta-analysis found five of five studies with high to very high risk of bias. Conclusions: Overall, our findings demonstrate no statistical difference in odds ratios of liver injury in patients with PACS compared with COVID-negative controls. As such, routine assessment and monitoring of liver injury in patients with PACS may not be required; however, higher quality data with lower risk of bias are required to make recommendations of higher certainty.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".