The Prevalence of Acute Kidney Injury in Patients Hospitalized With COVID-19 Infection: A Systematic Review and Meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Coronavirus disease 2019 (COVID-19) may be associated with high rates of acute kidney injury (AKI) and kidney replacement therapy (KRT), potentially overwhelming health care resources. Our objective was to determine the pooled prevalence of AKI and KRT among hospitalized patients with COVID-19. STUDY DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, Embase, the Cochrane Library, and a registry of preprinted studies, published up to October 14, 2020. STUDY SELECTION: Eligible studies reported the prevalence of AKI in hospitalized patients with COVID-19 according to the Kidney Disease: Improving Global Outcomes (KDIGO) definition. DATA EXTRACTION & SYNTHESIS: We extracted data on patient characteristics, the proportion of patients developing AKI and commencing KRT, important clinical outcomes (discharge from hospital, ongoing hospitalization, and death), and risk of bias. OUTCOMES & MEASURES: We calculated the pooled prevalence of AKI and receipt of KRT along with 95% CIs using a random-effects model. We performed subgroup analysis based on admission to an intensive care unit (ICU). RESULTS: = 88%) commenced KRT. LIMITATIONS: There was significant heterogeneity among the included studies, which remained unaccounted for in subgroup analysis. CONCLUSIONS: AKI complicated the course of nearly 1 in 3 patients hospitalized with COVID-19. The risk for AKI was higher in critically ill patients, with a substantial number receiving KRT at rates higher than the general ICU population. Because COVID-19 will be a public health threat for the foreseeable future, these estimates should help guide KRT resource planning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.002 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it