Bibliographic record
Abstract
BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disease and accounts for 5 - 10% of end stage renal disease. Mutations of two genes, PKD1 and PKD2, account for ∼ 85 and ∼ 15% of cases, respectively. OBJECTIVE: This paper reviews the clinical features of ADPKD, highlights the current roles for image- and molecular-based diagnostics, and the potential for new innovations to improve the clinical diagnostics for ADPKD. METHODS: This paper reviews the literature on the clinical features, differential diagnosis, and image- and molecular-based diagnostics for ADPKD. RESULTS/CONCLUSION: At present, presymptomatic diagnosis of ADPKD in subjects born with 50% risk is typically performed by renal ultrasonography. Renal MRI, with improved sensitivity for detecting smaller cysts, is a promising modality. There is also a clear role for molecular diagnostics, especially in patients with equivocal imaging results, in those with a negative family history and in younger at-risk subjects with a negative ultrasound study being evaluated as a living-related kidney donor. Also, several classes of promising disease-modifying drugs are being tested in clinical trials and, if proved effective, some of them will be used in early disease. Therefore, it is likely that there will be an increased demand for accurate and early diagnosis of ADPKD in the not so distant future.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".