Prevalence and Severity of Polycystic Liver Disease (PLD) in Autosomal Dominant Polycystic Kidney Disease (ADPKD)
Bibliographic record
Abstract
Background: Liver cysts are common in patients with ADPKD, but the prevalence of clinically significant PLD has not been well defined. Here we report the prevalence, severity and clinical characteristics of PLD from the Toronto Genetic Epidemiology Study of PKD (TGESP), a large prospective cohort study of patients with ADPKD from the Greater Toronto Area. Methods: All study patients underwent a research protocol with detailed clinical, laboratory (including genetic testing) and abdominal MRI between 2013-2023. An automate liver segmentation model was applied on coronal T2 MRI to calculate TLV and HtTLV. We analyzed baseline clinical characteristics and liver volume measurements of 617 study patients who were found to have a PKD1 protein-truncating (PT), PKD1 (inframe insertion/deletion), PKD1 non-truncating (NT), or PKD2 mutation. Patients were divided into quartiles based on their HtTLV and clinical characteristics, shown in Table 1. Results: The mean ± sd of TLV and HtTLV of the study cohort were 2163 ± 1466 ml and 1251 ± 856 ml/m; 57% were female. In total, 25% of patients had high HtTLV ranging between 1309 to 8810 ml/m (Figure 1A). The average HtTLV was larger in patients with PKD1 PT mutations in all age groups. However, the difference was only significant between PKD1 NT and PKD1 PT of age 50-59 years (p value: 0.02). Statistically significant positive correlations between HtTLV and TLV were seen among the three mutation classes (Figure 1B). Conclusion: Up to 25% of patients with ADPKD have moderate to severe PLD (i.e. 2.5 to 10x normal LV). TLV and volume growth rate appeared to be higher in PKD1 PT patients. Multivariate analysis including age, sex, body mass index will be performed to delineate the effects of mutation class on PLD severity. Funding: Government Support – Non-U.S.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".