Atypical Polycystic Kidney Disease by Imaging
Bibliographic record
Abstract
Abstract Using age- and height-adjusted total kidney volume, the Mayo Clinic Imaging Classification provides a validated approach to assess the risk of chronic kidney disease (CKD) progression in autosomal dominant polycystic kidney disease (ADPKD), but requires excluding patients with atypical imaging patterns, whose clinical characteristics have been poorly defined. We report an analysis of the prevalence, clinical and genetic characteristics of patients with atypical polycystic kidney disease by imaging. Patients from the extended Toronto Genetic Epidemiology Study of Polycystic Kidney Disease recruited between 2016 and 2018 completed a standardized clinical questionnaire, kidney function assessment, genetic testing, and kidney imaging by magnetic resonance or computed tomography. We compared the prevalence, clinical features, genetics, and renal prognosis of atypical versus typical polycystic kidney disease by imaging. Forty-six of the 523 (8.8%) patients displayed atypical polycystic kidney disease by imaging; they were older (55 vs. 43 years; P < 0.001), and less likely to have a family history of ADPKD (26.1% vs. 74.6%; P < 0.001), a detectable PKD1 or PKD2 mutation (9.2% vs. 80.4%; P < 0.001), or progression to CKD stage 3 or stage 5 (P < 0.001). Patients with atypical polycystic kidney disease by imaging represent a distinct prognostic group with a low likelihood of progression to CKD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".