Hyperkalemia Treatment Strategies by Specialty in the TRACK Study: Interim Analysis
Bibliographic record
Abstract
Background: We compared baseline treatment strategies by healthcare provider (HCP) specialty from TRACK, a prospective, observational study designed to address the evidence gap regarding HCP decision making in patients with hyperkalemia (HK). Methods: TRACK enrolled adults with serum potassium (sK+) >5.0 mmol/L and recorded HCP management decisions for 12 months. HCPs were asked, but not required, to record their specialty. An interim analysis was conducted when 600 enrolled participants had completed 6 months. Initial treatment strategy by specialty was compared using Fisher’s exact or Pearson’s Chi-squared tests. Treatment objectives and planned treatment duration were analyzed descriptively. Results: Participants (N=1330) were enrolled (July 2022–December 2023) in the USA and Europe (mean age, 68±14 years; female, 31%; mean sK+, 5.6±0.5 mmol/L; estimated glomerular filtration rate, 28±21 mL/min/1.73 m2). In total, 55% had chronic kidney disease (CKD) without heart failure (HF), 29% had CKD and HF, and 6% had HF without CKD. Overall, nephrologists managed 597 (45%) participants, 327 (25%) had another specified specialty (237 cardiologists), and the HCP specialty was not specified for 30%. Nephrologists were more likely to plan for indefinite treatment, cite CKD guideline compliance as an objective, and prescribe a low K+ diet (Table; P<0.0001); less likely to manage renin-angiotensin-aldosterone system inhibitor therapy as an initial strategy and more likely to manage K+ binder therapy (both P<0.0001). Conclusion: HCP specialty affected HK management approaches, possibly reflecting differences in specialty guidelines. Funding: Commercial Support - AstraZeneca
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".