Design and cohort characteristics of TRACK, a prospective study of hyperkalaemia management decision-making
Bibliographic record
Abstract
Background: binders with dose reduction of renin-angiotensin-aldosterone system inhibitors as a last resort. The extent to which these recommendations are implemented is uncertain, as real-world data on hyperkalaemia management are limited. The Tracking Treatment Pathways in Adult Patients with Hyperkalemia (TRACK) study is a multinational, prospective, longitudinal study that is being conducted to address this knowledge gap. We report the design and baseline cohort characteristics of this real-world study of hyperkalaemia management decision-making. Methods: This study enrolled participants within 21 days of an episode of hyperkalaemia in four European countries (UK, Spain, Germany, Italy) and the USA. During the 12-month follow up, data collected will include participant and healthcare provider characteristics (specialty and practice setting), hyperkalaemia treatment objectives and strategies, rationale for management decisions and indicators of response and patient-reported perceptions of their hyperkalaemia treatment. Results: The enrolled cohort includes 1330 participants, mean age 68 years, of whom 31% were women. At baseline, 6% reported heart failure, 55% chronic kidney disease, 29% both and 9% neither. Most participants (57%) were taking an angiotensin-converting enzyme inhibitor, angiotensin receptor blocker or angiotensin receptor/neprilysin inhibitor at baseline. Mineralocorticoid receptor antagonist use was lower (14%). Conclusions: The prospective TRACK study will shed light on practitioners' hyperkalaemia management decision-making and assess the impact of their decisions on hyperkalaemia recurrence. Understanding practitioners' underlying thought processes will facilitate efforts to improve hyperkalaemia management.ClinicalTrials.gov: NCT05408039.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".