Personalising renal function monitoring and interventions in people living with heart failure: a protocol for co-designing a care pathway in the RENAL-HF programme
Bibliographic record
Abstract
BACKGROUND: Heart failure affects almost one million people in the UK and is increasing in prevalence. Many drugs used to treat heart failure impair renal function and can lead to hospitalisation. Adverse drug problems can be partially mitigated through regular renal monitoring and optimising of drug dose and choice to prevent deterioration of kidney function. This protocol describes part of a wider research programme: personalising renal function monitoring and interventions in people living with heart failure (RENAL-HF). AIM: The aim of RENAL-HF is to develop improved processes in primary care to manage kidney health in people living with heart failure. METHOD: The protocol covers gathering views of healthcare professionals, patients, and carers, to co-develop a care pathway for use in primary care. Using a mixed-methods approach, the work comprises the following six stages: (1) understanding current practice of optimising heart failure treatment while preserving renal function; (2) co-designing a care pathway including personalised renal function monitoring, thresholds for intervention and clinical guidelines; (3) decision making to identify elements that will support the care pathway; (4) developing training materials for primary care to enable use of the care pathway; (5) testing the usability of the prototype care pathway; and 6) a feasibility and acceptability study to inform the pre-clinical development and usability of the care pathway ahead of a cluster randomised control trial (RCT). CONCLUSION: All stages will elicit evidence from primary care practices, practitioners, and patients with which to assess and refine the care pathway. The evidence will inform how algorithm-guided individualised treatment can be implemented to improve the outcomes of patients with heart failure.
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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 | 0.090 | 0.084 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.054 | 0.014 |
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".