#3124 How well do risk assessment guidelines perform for ADPKD?
Bibliographic record
Abstract
Abstract Background and Aims The approval of Tolvaptan for treatment of ADPKD heralds a new era when mechanism-based therapy is now possible. However, Tolvaptan is an expensive drug associated with potentially serious side effects and is currently reserved for patients at high-risk for progression to ESKD. Two sets of risk assessment guidelines for ADPKD are now available based on the consensus of two panels of nephrologists from Canada and Europe. However, how well do these guidelines perform in risk assessment has not been formally assessed. Method We conducted a prospective study in 474 patients with typical imaging pattern of ADPKD by MRI who also had detailed clinical and laboratory data. We used age- and height-adjusted total kidney volume to derive the Mayo Clinic Imaging Class as a “gold-standard” for risk assessment (i.e. low-risk: 1A-1B; high-risk: 1C-1E). We then applied the revised Canadian guidelines (Can J Kidney Health Dis. 2018) and the updated European guidelines (NDT 2022) to our patient cohort to assess their performance. Results The study cohort consisted of 286/474 (60%) high-risk patients with MCIC 1C-1E. Applying the updated Canadian risk assessment algorithm resulted in exclusion of 245/474 (52%) patients including 86/286 (30%) of the high-risk patients. The resultant cohort (229/474) was enriched with 88% high-risk patients but also included 12% of low-risk patients. The updated European guidelines provide a 3-step hierarchal algorithm, when applied resulted in exclusion of 55% (157/286) of high-risk patients. During the last step, 122 patients could not be classified due to a lack of eGFR slope information. The resultant cohort (180/474) was enriched with 72% high-risk patients but also included 28%(51/180) of low-risk patients. Conclusion Risk assessment in ADPKD is an evolving process that needs to be redefined by new clinical data and test technologies. Clinical guidelines should be evaluated using real-life data and those that enrich high-risk patients while minimizing low-risk patients have the most clinical utility
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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 | 0.029 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".