Changes in lipid, liver, and renal test profiles among patients with severe COVID-19 during and after hospital admission at Saint Peter Specialized Hospital, Addis Ababa, Ethiopia
Bibliographic record
Abstract
Abstract Objective: The progression of COVID-19 affects multiple organs, abnormal lipid, liver, and renal function tests have beenreported. Hence, this study aimed to determine differences in organ function and lipid profile among patients with severe COVID-19 during and after hospital admission. Methods: A follow-up study was conducted among COVID-19-admitted patients at St. Peter Specialized Hospital from January 1, 2021, to April 30, 2021. A total of 162 patients were included in the study. Five millilitersof venous blood was collected during admission and on the verge of discharge. Lipid, renal and liver function tests were performedusing aCobas 311 analyser. The data were entered and analysed with SPSS version 25. Results: The mean differences in total cholesterol, HDL, and LDL at admission and discharge were 20.13 (95% CI; 13.41-26.84; P<0.001), 7.53 (95% CI; 5.24-9.81; P <0.001), and 0.10 (95% CI; 0.06-0.14; P<0.001), respectively. Albumin concentrationincreased significantly at discharge, while the ALT concentration decreasedsignificantly at discharge (P<0.05). Conclusion: Dyslipidemia and low levels of Albumin were recorded during the progression of COVID-19 (at admission). This indicated severe COVID-19 disease leads to lipid alteration and Additional studies need to better define the disease's association with liver and renal function tests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".