Differential Bias for Creatinine- and Cystatin C- Derived Estimated Glomerular Filtration Rate in Critical COVID-19
Bibliographic record
Abstract
COVID-19 is a systemic disease, frequently affecting kidney function. Dexamethasone is standard treatment in severe COVID-19 cases, and is considered to increase plasma levels of cystatin C. However, this has not been studied in COVID-19. Glomerular filtration rate (GFR) is a clinically important indicator of renal function, but often estimated using equations (eGFR) based on filtered metabolites. This study focuses on sources of bias for eGFRs (mL/min) using a creatinine-based equation (eGFRLMR) and a cystatin C-based equation (eGFRCAPA) in intensive-care-treated patients with COVID-19. This study was performed on 351 patients aged 18 years old or above with severe COVID-19 infections, admitted to the intensive care unit (ICU) in Uppsala University Hospital, a tertiary care hospital in Uppsala, Sweden, between 14 March 2020 and 10 March 2021. Dexamethasone treatment (6 mg for up to 10 days) was introduced 22 June 2020 (n = 232). Values are presented as medians (IQR). eGFRCAPA in dexamethasone-treated patients was 69 (37), and 74 (46) in patients not given dexamethasone (p = 0.01). eGFRLMR was not affected by dexamethasone. eGFRLMR in females was 94 (20), and 75 (38) in males (p = 0.00001). Age and maximal CRP correlated negatively to eGFRCAPA and eGFRLMR, whereas both eGFR equations correlated positively to BMI. In ICU patients with COVID-19, dexamethasone treatment was associated with reduced eGFRCAPA. This finding may be explained by corticosteroid-induced increases in plasma cystatin C. This observation is important from a clinical perspective since adequate interpretation of laboratory results is crucial.
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.
How this classification was reachedexpand
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".