Royal Free Hospital‐estimated glomerular filtration rate for prognostic stratification of first acute kidney injury in cirrhosis
Bibliographic record
Abstract
BACKGROUND & AIMS: Renal function is a major determinant of prognosis in patients with cirrhosis. Current guidelines only contemplate serum creatinine (sCr) to assess kidney injury. However, there are formulas to estimate glomerular filtration rate (eGFR) which better measure renal function in patients listed for liver transplantation. There is no data available on whether these formulas predict prognosis in patients with acute kidney injury (AKI). METHODS: In 143 patients presenting with a first episode of AKI, we compared the prognostic value of renal function estimated using sCr or eGFR assessed with Modification of Diet in Renal Disease (MDRD-6), chronic kidney disease epidemiology (CKD-EPI) and Royal Free Hospital (RFH) for renal replacement therapy (RRT) within 30 days of AKI, and 30- and 90-day transplant-free survival. RESULTS: eGFR was calculated on values obtained before and at admission, at presentation of AKI (D0) and 48 hours after AKI (D2).15% of patients (more commonly in alcohol + metabolic etiology; P = .049 vs other) required RRT. Transplant-free survival at 30-and 90-day were 77% and 63%. Among sCr, MDRD-6, CKD-EPI and RFH-eGFR, the latter predicted best RRT (HR 0.937 95% CI 0.893-0.982, P = .007), 30-d (HR 0.936 95% CI 0.901-0.972, P = .001) and 90-d (HR 0.934 95% CI 0.908-0.972, P < .001) mortality/OLT. CONCLUSIONS: Renal function estimated using the RFH-eGFR calculated at D2 after AKI diagnosis is a strong predictor of RRT and of 30-d and 90-d transplant-free survival. Results suggest that in cirrhosis, RFH-eGFR may be a better indicator of prognosis in AKI than sCr.
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 | 0.000 |
| 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) | 0.000 | 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 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".