Methodological issues in clinical trials of polycystic kidney disease: a focused review
Bibliographic record
Abstract
The field of therapeutics in autosomal dominant polycystic kidney disease (ADPKD) has seen a significant expansion recently, as major clinical trials have provided promising evidence in favor of new disease-modifying drugs. Though these trials are encouraging, limitations are noticeable in the form of methodological issues that restrict the interpretation of results. In this review, we discuss the methodological pitfalls of high-profile clinical interventional trials for ADPKD which have been published since 2009. Issues in study design, patient selection and follow-up, analyses and reporting of results are presented. From this review, we highlight a number of suggestions for future improvement including designs to enrich a more homogeneous patient population (i.e. based on their age-adjusted total kidney volume and/or underlying mutation class) at high-risk for disease progression, appropriate study duration and patient sample size that are matched to the disease severity of the study patients, and the use of baseline characteristics (i.e. renal function, TKV, and the proportion of PKD1 and PKD2 patients) of the analyzed patients as a quality control measure to assess any potential imbalance in randomization. Furthermore, the recognition that TKV change is not a linear trait is important in both the study design and interpretation. Implementing these lessons learned from the published trials will greatly enhance the robustness and validity of future clinical trials in ADPKD.
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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 | 0.078 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".