Predictive value of serum cystatin C for risk of mortality in severe and critically ill patients with COVID-19
Bibliographic record
Abstract
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has rapidly evolved into a global pandemic. COVID-19 is clinically categorized into mild, moderate, severe, and critical illness. Acute kidney injury is an independent risk factor for poor prognosis in patients with. Serum cystatin C (sCys C) is considered a more sensitive biomarker for early renal insufficiency than conventional indicators of renal function. Early detection of risk factors that affect the prognosis of severe and critically ill patients while using active and effective treatment measures is very important and can effectively reduce the potential mortality rate. AIM: To determine the predictive value of sCys C for the prognosis of patients with COVID-19. METHODS: The clinical data of 101 severe and critically ill patients with COVID-19 at a designated hospital in Wuhan, Hubei Province, China were analyzed retrospectively. According to the clinical outcome, the patients were divided into a discharge group (64 cases) and a death group (37 cases). The general information, underlying diseases, and laboratory examination indexes of the two groups were compared. Multivariate Cox regression was used to explore the relationship between sCys C and prognosis. The receiver operating characteristic (ROC) curve was used to demonstrate the sensitivity and specificity of sCys C and its optimal cut-off value for predicting death. RESULTS: < 0.001). The area under the ROC curve was 0.755 (95%CI: 1.300-2.527), the cut-off value was 0.80, the specificity was 0.562, and the sensitivity was 0.865. CONCLUSION: sCys C is an independent risk factor for death in patients with COVID-19. Patients with a sCys C level of 0.80 mg/L or greater are at a high risk of death.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".