Imaging-Based Diagnosis of Autosomal Dominant Polycystic Kidney Disease
Bibliographic record
Abstract
The clinical use of conventional ultrasonography (US) in autosomal dominant polycystic kidney disease (ADPKD) is currently limited by reduced diagnostic sensitivity, especially in at-risk subjects younger than 30 years of age. In this single-center prospective study, we compared the diagnostic performance of MRI with that of high-resolution (HR) US in 126 subjects ages 16-40 years born with a 50% risk of ADPKD who underwent both these renal imaging studies and comprehensive PKD1 and PKD2 mutation screening. Concurrently, 45 healthy control subjects without a family history of ADPKD completed the same imaging protocol. We analyzed 110 at-risk subjects whose disease status was unequivocally defined by molecular testing and 45 unaffected healthy control subjects. Using a total of >10 cysts as a test criterion in subjects younger than 30 years of age, we found that MRI provided both a sensitivity and specificity of 100%. Comparison of our results from HR US with those from a previous study of conventional US using the test criterion of a total of three or more cysts found a higher diagnostic sensitivity (approximately 97% versus approximately 82%) with a slightly decreased specificity (approximately 98% versus 100%) in this study. Similar results were obtained in test subjects between the ages of 30 and 40 years old. These results suggest that MRI is highly sensitive and specific for diagnosis of ADPKD. HR US has the potential to rival the diagnostic performance of MRI but is both center- and operator-dependent.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".