Bibliographic record
Abstract
The traditional diagnostic criteria of renal dysfunction in cirrhosis are a 50% increase in serum creatinine (SCr) with a final value above 1.5 mg/dL. This means that patients with milder degrees of renal dysfunction are not being diagnosed, and therefore not offered timely treatment. The International Ascites Club in 2015 adapted the term acute kidney injury (AKI) to represent acute renal dysfunction in cirrhosis, and defined it by an increase in SCr of 0.3 mg/dL (26.4 µmoL/L) in <48 hours, or a 50% increase in SCr from a baseline within ≤3 months. The severity of AKI is described by stages, with stage 1 represented by these minimal changes, while stages 2 and 3 AKI by 2-fold and 3-fold increases in SCr respectively. Hepatorenal syndrome (HRS), renamed AKI-HRS, is defined by stage 2 or 3 AKI that fulfils all other diagnostic criteria of HRS. Various studies in the past few years have indicated that these new diagnostic criteria are valid in the prediction of prognosis for patients with cirrhosis and AKI. The future in AKI diagnosis may include further refinements such as inclusion of biomarkers that can identify susceptibility for AKI, differentiating the various prototypes of AKI, or track its progression.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".