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: This review will summarize and discuss the role of cystatin C in the diagnosis of acute kidney injury. RECENT FINDINGS: Cystatin C is easily measured and has the characteristics of an ideal marker of kidney function. Data suggest that cystatin C is modified by age, sex, muscle mass, obesity, smoking status, thyroid function, inflammation, and malignancy. These factors suggest the need for age-specific and sex-specific reference standards. Cystatin C-based glomerular filtration rate estimates may perform better than creatinine in selected patient populations (elderly, children, transplantation, cirrhosis, malnourished). Cystatin C has been evaluated for the early diagnosis of acute kidney injury (AKI) in several populations. Serum cystatin C has value for the diagnosis of acute kidney injury; however, it has often performed similarly to creatinine. Urinary cystatin C has potential as an early marker. SUMMARY: Cystatin C is an accurate biomarker for the early detection of AKI, and may, in selected populations, be superior to creatinine; however, data have been inconsistent. It also has reasonable discrimination for important outcomes such as death and renal replacement therapy (RRT). Additional studies are needed that focus on the cost-effectiveness of earlier detection of AKI with cystatin C compared with creatinine, and whether these biomarkers have complementary value.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 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