Bibliographic record
Abstract
PURPOSE OF REVIEW: Fibrosis is an important biomarker of chronic kidney injury, and a powerful predictor of renal outcome. Currently, the only method for measuring fibrotic burden is histologic analysis, which requires a kidney biopsy in humans, or kidney removal in animal models. These requirements have not only hindered our ability to manage patients effectively, but have also prevented a full understanding of renal fibrosis pathogenesis, and slowed the translation of new antifibrotic agents. The development of noninvasive fibrosis imaging tools could thus transform both clinical care and renal fibrosis research. RECENT FINDINGS: Conventional imaging modalities have historically failed to image fibrosis successfully. However, recent exciting technological advances have greatly enhanced their capabilities. New techniques, for example, may allow imaging of the physical consequences of scarring, as surrogate measures of renal fibrosis. Similarly, other groups have developed ways to directly image extracellular matrix, either with the use of contrast-enhanced probes, or using matrix components as endogenous contrast agents. SUMMARY: New developments in imaging technology have the potential to transform our ability to visualize renal fibrosis and to monitor its progression. In doing so, these advances could have major implications for kidney disease care, the development of new antiscarring agents, and our understanding of renal fibrosis in general.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".