Randomised, blinded, cross-over evaluation of the palatability of and preference for different potassium binders in participants with chronic hyperkalaemia in the USA, Canada and Europe: the APPETIZE study
Bibliographic record
Abstract
Objectives Traditional potassium (K + ) binders for treating hyperkalaemia are unpalatable and poorly tolerated. Newer K + binders are reportedly better tolerated; however, no published data describe their palatability, a determinant of long-term adherence. This study evaluated the palatability of and preference for three K + binders: sodium and calcium polystyrene sulfonate (S/CPS), sodium zirconium cyclosilicate (SZC) and calcium patiromer sorbitex (patiromer). Design Phase 4, randomised, participant-blinded, cross-over study. Participants were randomised to one of six taste sequences and, using a ‘sip and spit’ approach, tasted each K + binder before completing a survey. Setting 17 centres across the USA, Canada and European Union. Participants 144 participants with chronic kidney disease, hyperkalaemia and no recent use of K + binders. Main outcome measures For the primary (USA) and key secondary (Canada and European Union) endpoints, participants rated palatability attributes (taste, texture, smell and mouthfeel) and willingness to take each K + binder on a scale of 0–10 (rational evaluation). Feelings about each attribute, and the idea of taking the product once daily, were evaluated using a non-verbal, visual measure of emotional response. Finally, participants ranked the K + binders according to palatability. Results In each region, SZC and patiromer outperformed S/CPS on overall palatability (a composite of taste, texture, smell and mouthfeel), based on rational evaluation and emotional response. Taking the product once daily was more appealing for SZC and patiromer, creating greater receptivity than the idea of taking S/CPS. The emotional response to mouthfeel had the strongest influence on feelings about taking each product. In each region, a numerically greater proportion of participants ranked SZC as the most preferred K + binder versus patiromer or S/CPS. Conclusions Preference for more palatable K + binders such as SZC and patiromer may provide an opportunity to improve adherence to long-term treatment of hyperkalaemia. Trial registration number NCT04566653 .
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".