A Prospective, Real-World Evidence Study of Hyperkalemia Management Decision Making: Design of the TRACK Study
Bibliographic record
Abstract
Background: Prospective data on healthcare professionals' (HCP) decision making and patient management related to hyperkalemia (HK) are scarce. The TRACK study will collect data on HCP objectives and decision-making behaviors when encountering patients with HK in real-world practice, as well as patient perceptions of HK and its treatment. Methods: TRACK is a multinational, prospective, observational, longitudinal cohort study within the US and Europe. We plan to enroll approximately 1250 patients with established HK. During the 12-month follow-up, data will be collected from health records and HCPs using an electronic case report form at 3-month intervals. Patient-reported outcomes will also be collected. The primary objective is to describe HK management decisions, their rationale, and expectations at baseline, and their association with treatment response indicators (correction of HK; target doses of renin-angiotensin-aldosterone system inhibitors [RAASi]; healthcare resource utilization) (Figure). The secondary objective is to describe patients' clinical parameters during follow-up. Exploratory objectives include patient awareness and satisfaction with HK management. Results: Anticipated study completion is 2024. Conclusions: This non-interventional, real-world study will gather insights into HCP approaches to implementing HK management. TRACK will characterize the impact of HCP decision making on HK recurrence, inform the use of guideline-directed therapies related to RAASi use, and address knowledge gaps regarding HCP and patient perspectives on HK management. Funding: Commercial Support - AstraZenecaTRACK Study Design Concept
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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 | 0.037 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".