Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: The early detection of acute kidney injury may allow for timely preventive or therapeutic measures. This review discusses the role of traditional and novel biomarkers in early acute kidney injury diagnosis. RECENT FINDINGS: Detection of acute kidney injury relies on changes in serum creatinine and urea. These are not ideal and do not reflect genuine injury or real-time changes in kidney function. Several novel biomarkers have emerged for early detection of acute kidney injury. Cystatin C is sensitive to early and mild changes to kidney function. Neutrophril gelatinase-associated lipocalin is expressed early after injury and has value in predicting acute kidney injury after kidney transplant and cardiopulmonary bypass. Interleukin-18 has been detected early in acute kidney injury after kidney transplant, cardiopulmonary bypass and sepsis. Kidney injury molecule-1 is upregulated after ischemic/toxic injury and has the ability to predict the need for renal replacement therapy and mortality. While heterogeneous in their expression, these biomarkers may have value as a sequential 'panel' to aid in detecting, classifying and predicting the clinical course of acute kidney injury. SUMMARY: The early detection of acute kidney injury is a clinical and research priority. Traditional measures may contribute to delayed acute kidney injury diagnosis. Recent biomarkers have promise for earlier detection and for research into novel interventions.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it