Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is the most common mendelian disorder of the kidney and accounts for ~5% of end-stage renal disease in North America. It is characterized by focal development of renal cysts which increase in number and size with age. Mutations of PKD1 and PKD2 account for most cases. Although the clinical manifestations of both gene types overlap completely, PKD1 is associated with more severe disease than PKD2, with larger kidneys and earlier onset of end-stage renal disease. Furthermore, marked within-family renal disease variability is well documented in ADPKD and suggests a strong modifier effect from as yet unknown genetic and environmental factors. In turn, the significant inter- and intra-familial renal disease variability poses a challenge for diagnosis and genetic counseling. In general, renal ultrasonography is commonly used for the diagnosis, and age-dependent criteria have been defined for subjects at risk of PKD1. However, the utility of the PKD1 ultrasound criteria in the clinical setting is unclear since their performance characteristics have not been defined for the milder PKD2 and the gene type for most test subjects is unknown. Recently, highly predictive ultrasound diagnostic criteria have been derived for at-risk subjects of unknown gene type. Additionally, both DNA linkage and gene-based direct sequencing are available for the diagnosis of ADPKD, especially in subjects with equivocal imaging results, a negative or indeterminate family history, or in younger at-risk individuals being evaluated as potential living related kidney donor. This review will highlight the utility and limitations of clinical predictors of gene types, imaging- and molecular-based diagnostic tests, and present an integrated approach for evaluating individuals suspected to have ADPKD.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".