Improving the quality of the performance and delivery of continuous renal replacement therapy (CRRT) to critically ill patients across a healthcare system: QUALITY CRRT: a study protocol
Bibliographic record
Abstract
INTRODUCTION: Continuous renal replacement therapy (CRRT) is a continuous form of dialysis used to support critically ill patients with acute kidney injury. The ideal delivery of CRRT requires ongoing monitoring and reporting to adjust practice and deliver optimal therapy. However, this practice occurs variably. METHODS: QUALITY CRRT is a multicentre, prospective, stepped-wedged, interrupted time series (ITS) evaluation of the effectiveness, safety and cost of implementing a multifaceted CRRT quality assurance and improvement programme across an entire healthcare system. This study will focus on the standardisation of CRRT programmes with similar structure, process and outcome metrics by the reporting of CRRT key performance indicators (KPIs). The primary outcome will be the quarterly performance of CRRT KPIs. Secondary outcomes will include patient-centred outcomes and economic outcomes. Analysis will compare pre-implementation and post-implementation groups as well as for the performance of KPIs using an ITS methodology. The health economic evaluation will include a within-study analysis and a longer-term model-based analysis. DISCUSSION: The effective delivery of CRRT to critically ill patients ideally requires a standardised approach of best practice assessment and ongoing audit and feedback of standardised performance measures. QUALITY CRRT will test the application of this strategy stakeholder engagement and stepped-wedged implementation across an entire healthcare system. ETHICS AND DISSEMINATION: This study has received ethics approval. We will plan to publish the results in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT04221932. PROTOCOL VERSION: 1.0 (15 June 2020).
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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 | 0.114 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".