Total Kidney Volume (TKV) Is Associated With Health-Related Quality of Life (HRQoL) Among Patients With Autosomal Dominant Polycystic Kidney Disease (ADPKD)
Bibliographic record
Abstract
Background: ADPKD is the most common hereditary kidney disease and imposes significant physical and emotional burden on affected patients. The association of healthrelated quality-of-life (HRQoL) with disease severity markers (DSMs) including total kidney volume (TKV) has been assessed in a few previous studies, but results were inconclusive, in part due to small sample size. In this cross-sectional study we assessed the association between TKV and domains assessed by the short for (SF)-36 questionnaire in a large cohort of patients with ADPKD recruited in Toronto between January 2017 to December 2021. Methods: Participants completed the study questionnaire that included questions about sociodemographic characteristics and the SF-36 questionnaire. Clinical data was abstracted from medical records. eGFR was estimated from serum creatinine using the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. All study patients had their TKV measured by MRI and completed a comprehensive PKD1 and PKD2 mutation screen. In addition to assess the association between HRQoL scores and TKV, both as continuous variables, we also generated binary HRQoL variable defining poor HRQoL as the lowest quartile; this quartile then was compared to the rest of the sample. Results: Of the 306 participants (mean[SD] age 49[15] years) 45% were male. Mean (SD) eGFR was 79(27) ml/min/1.73m2, 12 (4%) of participants had eGFR <30, no patients were on dialysis. Median (interquartile range [IQR]) TKV was 567 (338-933) ml, 149 (49%) had TKV > 1000 ml. Of the SF-36 domains physical function (rho=-0.28, p<0.001) general health perception (rho=-0.19, p<0.001), bodily pain (rho-0.13, p=0.02) and the physical component score (rho=-0.26, p<0.001) correlated with TKV. Mutation class was not associated with HRQoL domains. In multivariable regression models (adjusted for age, sex and eGFR) TKV was significantly associated with the general health perception domain. Conclusions: In patients with ADPKD, TKV was significantly correlated with several HRQoL SF-36 domains. Some of these associations were confounded by age and eGFR, but poor general health perceptions remained independently associated with TKV. 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".