A proposed algorithm for initiation of renal replacement therapy in adult critically ill patients
Bibliographic record
Abstract
Critically ill patients whose course is complicated by acute kidney injury often receive renal replacement therapy (RRT). For these patients, initiation of RRT results in a considerable escalation in both the complexity and associated cost of care. While RRT is extensively used in clinical practice, there remains uncertainty about the ideal circumstances of when to initiate RRT and for what indications. The process of deciding when to initiate RRT in critically ill patients is complex and is influenced by numerous factors, including patient-specific and clinician-specific factors and those related to local organizational/logistical issues. Studies have shown marked variation between clinicians, and across institutions and countries. As a consequence, analysis of ideal circumstances under which to initiate RRT is challenging. Recognizing this limitation, we review the available data and propose a clinical algorithm to aid in the decision for RRT initiation in critically ill adult patients. The algorithm incorporates several patient-specific factors, based on evidence when available, that may decisively influence when to initiate RRT. The objective of this algorithm is to provide a starting point to guide clinicians on when to initiate RRT in critically ill adult patients. In addition, the proposed algorithm is intended to provide a foundation for prospective evaluation and the development of a broad consensus on when to initiate RRT in critically ill patients.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".