Unified Criteria for Ultrasonographic Diagnosis of ADPKD
Why this work is in the frame
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Bibliographic record
Abstract
Individuals who are at risk for autosomal dominant polycystic kidney disease are often screened by ultrasound using diagnostic criteria derived from individuals with mutations in PKD1. Families with mutations in PKD2 typically have less severe disease, suggesting a potential need for different diagnostic criteria. In this study, 577 and 371 at-risk individuals from 58 PKD1 and 39 PKD2 families, respectively, were assessed by renal ultrasound and molecular genotyping. Using sensitivity data derived from genetically affected individuals and specificity data derived from genetically unaffected individuals, various diagnostic criteria were compared. In addition, data sets were created to simulate the PKD1 and PKD2 case mix expected in practice to evaluate the performance of diagnostic criteria for families of unknown genotype. The diagnostic criteria currently in use performed suboptimally for individuals with mutations in PKD2 as a result of reduced test sensitivity. In families of unknown genotype, the presence of three or more (unilateral or bilateral) renal cysts is sufficient for establishing the diagnosis in individuals aged 15 to 39 y, two or more cysts in each kidney is sufficient for individuals aged 40 to 59 y, and four or more cysts in each kidney is required for individuals > or = 60 yr. Conversely, fewer than two renal cysts in at-risk individuals aged > or = 40 yr is sufficient to exclude the disease. These unified diagnostic criteria will be useful for testing individuals who are at risk for autosomal dominant polycystic kidney disease in the usual clinical setting in which molecular genotyping is seldom performed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it