A Pilot Study of Okkidney, A Phosphate Counting Application in Patients on Peritoneal Dialysis
Bibliographic record
Abstract
Background Hyperphosphatemia is associated with adverse outcomes in patients treated with peritoneal dialysis (PD). We have shown that a fixed meal phosphate binder dosing schedule is not appropriate. The purpose of this study was to evaluate the beta version of OkKidney, a phosphate counting app that matches meal phosphate content with binder dose. Methods A convenience sample of adult patients treated with PD completed a pre-survey that included the technology readiness index (TRI 2.0). After a short information session, patients used OkKidney for 30 days. Pre- and post-intervention serum calcium, serum phosphate, and calcium carbonate binder intake were collected and compared using a paired t-test. A post-intervention survey using a 5-point Likert scale was used to gather patient feedback. Results Ten patients (5M, 5F) completed the study protocol. Participants were 55 ± 17 years old, predominately Caucasian, retired (60%), and owned a smartphone (70%). The median TRI score was 3.66 (max 5), indicating a moderate level of readiness. The post-survey results indicated a favorable rating for ease of use (μ = 4.4 ± 0.84) and usefulness (μ = 4.3 ± 0.68) of OkKidney. The average serum phosphate ( p = 0.99) and calcium ( p = 0.68) were not different pre-/post-intervention, but calcium carbonate intake tended to decrease ( p = 0.12). Conclusion Patients reported a positive experience with OkKidney. Further patient-specific adjustments of the binder dose to meal phosphate content may be required to demonstrate a statistically significant decrease in phosphate levels. We believe a larger trial is warranted to investigate the clinical implications of this app.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".