Molecular Diagnostics in Autosomal Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Gene-based mutation screening is now available and has the potential to provide diagnostic confirmation or exclusion of autosomal dominant polycystic kidney disease. This study illustrates its utility and limitations in the clinical setting. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using a molecular diagnostic service, genomic DNA of one affected individual from each study family was screened for pathologic PKD1 and PKD2 mutations. Bidirectional sequencing was performed to identify sequence variants in all exons and splice junctions of both genes and to confirm the specific mutations in other family members. In two multiplex families, microsatellite markers were genotyped at both PDK1 and PKD2 loci, and pair-wise and multipoint linkage analysis was performed. RESULTS: Three of five probands studied were referred for assessment of renal cystic disease without a family history of autosomal dominant polycystic kidney disease, and two others were younger at-risk members of families with autosomal dominant polycystic kidney disease being evaluated as living-related kidney donors. Gene-based mutation screening identified pathogenic mutations that provided confirmation or exclusion of disease in three probands, but in the other two, only unclassified variants were identified. In one proband in which mutation screening was indeterminate, DNA linkage studies provided strong evidence for disease exclusion. CONCLUSIONS: Gene-based mutation screening or DNA linkage analysis should be considered in individuals in whom the diagnosis of autosomal dominant polycystic kidney disease is uncertain because of a lack of family history or equivocal imaging results and in younger at-risk individuals who are being evaluated as living-related kidney donors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".