SP033WHAT DIFFERENCE A FORMULA MAKES? ELIGIBILITY FOR TOLVAPTAN THERAPY FOR ADPKD - HOW TO SELECT THOSE WHO WILL BENEFIT THE MOST FROM TREATMENT?
Bibliographic record
Abstract
Introduction and Aims: Excitement about ADPKD has never been greater with the European regulatory approval of the use of Tolvaptan (Januvia) in progressive disease in 2015. But, what exactly constitutes progressive ADPKD? Tempo 3:4 was of course a trial examining the change in total kidney volume as the primary outcome. But in clinical practice this is hard - often impossible - to measure. GFR is easier to use, of course, but even here, there are nuances and considerations, not the least which CKD formula / methodology to deploy. We wanted to examine the impact of age, and of different CKD formulae, on the likely numbers of patients eligible for treatment, from our ADPKD cohort. In addition, we wanted to see how well BP is currently controlled in this condition, using the same cohort with reference to the HALT-PKD study from 2014. Methods: We searched our electronic databases and patient files to construct a "long list" of patients with ADPKD, of all ages, genders, races, and with an MDRD eGFR of 30-90 mls/min. We also then used clinical and laboratory data to complete an analysis of each patient, including their current BP levels in clinic, and, the type of BP treatment if any in use. We compared the results from MDRD formula derived eGFR to CKD-Epi creatinine (2009) formulae derived eGFR (all creatinines traceable). We audited the clinic BP against HALT-PKD criteria (intensive BP 95-110/60-75 mm Hg) and standard BP (120-130/70-80 mm Hg).
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".