Evaluation of Plasma Soluble Urokinase Plasminogen Activator Receptor Levels in Patients With COVID-19 and Non-COVID-19 Pneumonia: An Observational Cohort Study
Bibliographic record
Abstract
Background: The respiratory system is the main system affected by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and a great number of infected people need hospitalization. Soluble urokinase plasminogen activator receptor (suPAR) is a biomarker indicative of acute and chronic inflammation. Current literature supports that suPAR has great predictive ability for mortality in patients with coronavirus disease 2019 (COVID-19). The aim of this study was to compare the value of suPAR and other laboratory biomarkers in patients with chest infection and suspected COVID-19. Methods: A total of 41 consecutive patients with chest infection were enrolled in the study and were assigned into two groups according to the real-time polymerase chain reaction (PCR) result for SARS-CoV-2. The two groups had no significant difference in baseline data (age, sex), arterial oxygen partial pressure (PO 2 )/fraction of inspired oxygen (FiO 2 ) ratio and mortality. Results: Among patients with chest infection who required hospitalization, suPAR was significantly higher on admission in those with COVID-19 when compared to patients with non-COVID-19. suPAR had a great prognostic ability for in-hospital mortality in the COVID-19 subgroup. Conclusions: A single measurement of suPAR on admission can provide prognostic information for patients with suspected COVID-19 pneumonia. In the subgroup of patients with positive real-time PCR result for SARS-CoV2, suPAR was significantly higher and had an excellent prognostic value for the in-hospital mortality. J Clin Med Res. 2021;13(9):474-478 doi: https://doi.org/10.14740/jocmr4579
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".