Evolving role of genetic testing for the clinical management of autosomal dominant polycystic kidney disease
Bibliographic record
Abstract
Autosomal dominant polycystic kidney disease (ADPKD) is caused primarily by mutations of two genes, PKD1 and PKD2. In the presence of a positive family history of ADPKD, genetic testing is currently seldom indicated as the diagnosis is mostly based on imaging studies using well-established criteria. Moreover, PKD1 mutation screening is technically challenging due to its large size, complexity (i.e. presence of six pseudogenes with high levels of DNA sequence similarity) and extensive allelic heterogeneity. Despite these limitations, recent studies have delineated a strong genotype-phenotype correlation in ADPKD and begun to unravel the role of genetics underlying cases with atypical phenotypes. Furthermore, adaptation of next-generation sequencing (NGS) to clinical PKD genetic testing will provide a high-throughput, accurate and comprehensive screen of multiple cystic disease and modifier genes at a reduced cost. In this review, we discuss the evolving indications of genetic testing in ADPKD and how NGS-based screening promises to yield clinically important prognostic information for both typical as well as unusual genetic (e.g. allelic or genic interactions, somatic mosaicism, cystic kidney disease modifiers) cases to advance personalized medicine in the era of novel therapeutics for ADPKD.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".