Proactive Prescription-Based Fluid Management Versus Usual Care in Critically Ill Patients on Kidney Replacement Therapy (Probe-Fluid): A Pilot Clinical Trial Protocol
Bibliographic record
Abstract
Background: Severe acute kidney injury (AKI) in the intensive care unit (ICU) is complicated by fluid accumulation, making fluid removal a central component of kidney replacement therapy (KRT). However, the optimal fluid management strategy in patients receiving KRT remains unknown, and practice varies widely. Objective: To assess the feasibility of conducting a multicentre randomized controlled trial comparing a protocol-based fluid-removal strategy with usual care in critically ill adult patients receiving KRT. The primary objective is to determine whether the intervention results in a difference in cumulative fluid balance from randomization to day five. Design: Open-label, multicentre, pilot randomized controlled trial. Setting: Centers in Canada, the United States, and Australia. Patients: We are enrolling 150 adults admitted to the ICU with AKI who have been receiving KRT for ≤48 hours or who are anticipated to commence KRT within the next 12 hours. Measurements: The primary outcome is the difference in cumulative fluid balance (mL) between treatment arms from randomization (day zero) to the end of day five. Secondary outcomes include feasibility metrics, patient outcomes, resource use, safety outcomes, and process measures. Methods: Participants are randomized 1:1 to receive either protocol-based fluid management or usual care. The intervention consists of a prescription template updated at least once daily by the attending care team, specifying a 24-hour fluid balance target, a prescription for fluid removal using KRT, and daily re-evaluation of fluid intake. The intervention is continued until day five post-randomization, KRT discontinuation due to kidney recovery, or ICU discharge. Limitations: The application of the intervention relies on the clinical judgment of the attending care team, which may affect the fidelity of the intervention. Usual care may differ between institutions, which may lead to variability. The treatment teams are unblinded, however the statistician will be blinded to group allocation. Conclusions: The Probe-Fluid pilot trial will provide important groundwork toward a future definitive multicentre randomized controlled trial comparing a protocol-based fluid management strategy with usual care in critically ill patients receiving KRT.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".