Clinical Characteristics and Outcomes of Patients With Severe COVID-19 Induced Acute Kidney Injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The incidence and outcome of Coronavirus disease 2019 (COVID-19)-induced kidney injury have been variably described. We aimed to describe the clinical characteristics, correlates and outcomes of critically ill patients with severe COVID-19 complicated by acute kidney injury (AKI). METHODS: We performed a multicenter retrospective cohort study of 671 critically ill adults with laboratory-confirmed COVID-19 from 19 hospitals in China between January 1 to February 29, 2020. Data were captured on demographics, comorbidities, symptoms, acute physiology, laboratory parameters, interventions, and outcomes. The primary exposure was ICU admission for confirmed COVID-19 related critically illness. The primary outcome was 28-day mortality. Secondary outcomes included factors associated with AKI, organ dysfunction, treatment intensity, and health services use. MEASUREMENTS AND MAIN RESULTS: Of 671 severe COVID-19 patients (median [IQR] 65 [56-73] years; male sex 65% (n = 434); hypertension 43% (n = 287) and APACHE II score 10 [7-14]), 39% developed AKI. Patients with AKI were older, had greater markers of inflammation and coagulation activation, and had greater acuity and organ dysfunction as presentation. Despite similar treatment with antivirals, patients with AKI had lower viral conversion negative rates than those without AKI. The 28-day mortality was much higher in AKI patients than patients without AKI (72% vs. 42%), and there was an increase in 28-day mortality according to the severity of AKI. Non-survivors were less likely to receive antiviral therapy [132 (70%) vs. 65 (88%)] compared with survivors and have lower viral negative conversion rate [17 (9%) vs. 47 (64%)]. CONCLUSIONS: Acute kidney injury was quite common in severe COVID-19 pneumonia, which associated with higher mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.401 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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