The Quest for Renal Disease Proteomic Signatures: Where Should We Look?
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
Abstract Renal diseases are prevalent and important. However, despite significant strides in medicine, clinical nephrology still relies on nonspecific and inadequate markers such as serum creatinine and total urine protein for monitoring and diagnosis of renal disease. In case of glomerular renal diseases, biopsy is often necessary to establish the diagnosis. With new developments in proteomics technology, numerous studies have emerged, searching for better markers of kidney disease diagnosis and/or prognosis. Blood, urine, and renal biopsy tissue have been explored as potential sources of biomarkers. Some interesting individual or multiparametric biomarkers have been found; however, none have yet been validated or entered clinical practice. This review focuses on some studies of biomarkers of glomerular renal diseases, as well as addresses the question of which sample type(s) might be most promising in preliminary discovery phases of candidate proteins.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it