Comparison of Different Selection Strategies for Tolvaptan Eligibility among Autosomal Dominant Polycystic Kidney Disease Patients
Bibliographic record
Abstract
BACKGROUND: Tolvaptan can slow down renal function decline in autosomal dominant polycystic kidney disease (-ADPKD). While there is consensus across international recommendations that the drug should only be used in patients with high risk of rapid progression, identification criteria for rapid progression vary. Here, we investigated different assessment strategies using a real-life ADPKD cohort. METHODS: Observational retrospective cohort analysis. The study included 131 ADPKD patients aged 19-78 years who were referred to the Hannover Medical School outpatient clinic for evaluation of tolvaptan treatment. Six different assessment strategies for tolvaptan eligibility were tested for each patient. Comparative analysis for different assessments was performed in the total study population, the subpopulation with available computed tomography/magnetic resonance imaging data, and the genotyped subpopulation. RESULTS: Comparing 6 assessment strategies revealed strong variations in the individual selection processes resulting in treatment recommendations for 14.5-64.9% of patients. The highest patient number was selected by the Scottish and the lowest by the Japanese approach. Few patients had positive recommendations by all 6 systems, but strong congruency was observed between the Scottish, U.K. and Canadian patient selection. The lowest number of overlapping patients was found between the Japanese and the ERA-EDTA selection. Important discrepancies were also found between the ERA-EDTA and the U.S. system due to different emphases on parameters of kidney function versus kidney volume. Limitations of the study included the restricted sample size, heterogeneity in parameter availability and lack of outcome data. CONCLUSIONS: The study draws attention to important discrepancies between different decision algorithms for tolvaptan eligibility in ADPKD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".