Supplementary Material for: Potassium-Binding Resins: Associations with Serum Chemistries and Interdialytic Weight Gain in Hemodialysis Patients
Bibliographic record
Abstract
Background: Although potassium-binding sodium-based resins (K resins) have been prescribed to treat hyperkalemia for 50 years, there have been no large studies of their effects among hemodialysis (HD) patients. Methods: Data from 11,409 patients in the Dialysis Outcomes and Practice Patterns Study in Belgium, Canada, France, Italy, and Sweden (nations where ≥5% of patients were prescribed a sodium- based K resin; seven other countries had <5% use) between 2002 and 2011 were analyzed. Linear mixed models examined associations between K resin use and interdialytic weight gain (IDWG) and serum electrolyte concentrations. Mortality was analyzed using Cox regression. An instrumental variable approach was used to partially account for unmeasured confounders. Results: The K resin prescription rate was 20% overall. As hypothesized, patients prescribed a K resin had greater IDWG and higher serum bicarbonate, phosphorus, and sodium (but not calcium) concentrations. Patients prescribed a K resin had higher serum K levels, but serum K levels were lower in an instrumental variable analysis limiting treatment by indication bias. K resin use was not associated with mortality risk. Conclusion: We report the first large study of K resin use and associated laboratory and clinical outcomes in HD patients. The prescription rate of K resins varied dramatically by country and dialysis center. The results suggest that K resin use may effectively lower serum K, although at the expense of somewhat higher phosphatemia and greater IDWG, and had no clear association with mortality. Further study is warranted to elucidate the optimal role for K resins in modern dialysis care.
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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 | 0.001 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.598 | 0.073 |
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".