Sequential organ failure assessment (SOFA) score as a predictor of acute kidney injury in COVID-19 patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The sequential organ failure assessment (SOFA) score is a tool used to describe organ failure in critically ill patients. Studies have showed that coronavirus disease 2019 (COVID-19) patients who develop predictor of acute kidney injury (AKI) often have an increased SOFA. Objective: This study aimed to evaluate the potential of SOFA score as a predictor AKI in COVID-19 patients. Methods: A systematic search was conducted on PubMed, Google Scholar, EBSCO-Host, and ProQuest. The risk of bias was assessed using the Newcastle-Ottawa Scale . Result: Out of the 9 studies reviewed, 7 showed a significant association between SOFA score and AKI. The meta-analysis of 3 studies gave mean differences of 1.66 in favor of the AKI group (95% CI 1.12 - 2.21). The heterogeneity was low (Tau2 result = 0.06, I2 result = 22% with p = 0.28) and the significant results for the overall effect showed a value of p < 0.00001. Conclusion: The SOFA score has the potential to be a good predictor of AKI development in COVID-19 patients, with a significant mean difference.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".