Renal Cystic Diseases
Bibliographic record
Abstract
This review aims to assist in the categorization of inherited, developmental, and acquired cystic disease of the kidney as well as to provide a pertinent, up-to-date bibliography. The conditions included are autosomal-dominant polycystic kidney disease, autosomal-recessive polycystic kidney disease, unilateral renal cystic disease (localized cystic disease), renal simple cysts, multicystic dysplastic kidney, pluricystic kidney of the multiple malformation syndromes, juvenile nephronophthisis and medullary cystic disease, medullary sponge kidney, primary glomerulocystic kidney disease, and glomerulocystic kidney associated with several systemic disorders mainly of genetic or chromosomal etiology, cystic kidney in tuberous sclerosis, and in von Hippel-Lindau syndrome, cystic nephroma, cystic variant of congenital mesoblastic nephroma, mixed epithelial stromal tumor of the kidney, renal lymphangioma, pyelocalyceal cyst, peripylic cyst and perinephric pseudocyst, acquired renal cystic disease of long-term dialysis, and cystic renal cell carcinoma and sarcoma. Whereas the gross and histologic appearance of some of these conditions may be diagnostic, clinical and sometimes molecular studies may be necessary to define other types.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".