Bibliographic record
Abstract
Acute kidney injury remains a serious clinical problem for intensive care unit patients, and its incidence is rising. The detection and diagnosis of acute kidney injury in the intensive care unit currently require use of conventional markers of kidney function, specifically, serum creatinine and urea levels and, less frequently, other urinary tests. These conventional markers are familiar to clinicians and have long been used at the bedside. However, these markers are clearly not ideal, each has limitations, and none reflect real-time changes in glomerular filtration rate or a genuine acute injurious process to the kidney. More importantly, these conventional markers can contribute to delays in recognition of acute kidney injury and, hence, delays to appropriate supportive and therapeutic interventions. The early detection and diagnosis of acute kidney injury should be a clinical priority. A diagnostic test or panel of tests that are capable of evaluating aspects both of kidney function and acute injury are desperately needed in critical care nephrology. Cystatin C has been shown superior to conventional markers and may assume a greater role in intensive care unit patients for detecting both early changes in glomerular filtration rate and evidence of acute injury. Other newly characterized markers of kidney function or acute injury have the potential to revolutionized the field of critical care nephrology and greatly improve the supportive and therapeutic management of intensive care unit patients with acute kidney injury.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".