Association between acute kidney injury, delirium and outcomes in patients with critical illness: a protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Acute kidney injury (AKI) and delirium are common clinical complications of critical illness. Relatively few studies have evaluated the relationship between AKI and delirium. This systematic review will assess this association among critically ill patients. METHODS AND ANALYSIS: We will conduct comprehensive searches of databases, including Ovid MEDLINE, Ovid Embase, CINAHL, Scopus, Web of Science Core Collection and the Cochrane Library, using keywords to capture the existing literature related to AKI and delirium. Searches will range from inception to January 2025. Two reviewers will independently screen, select and extract studies using the web-based tool, Covidence. Inclusion criteria will include clinical trials or observational cohorts reporting both AKI and delirium in patients admitted to intensive care units. Case reports, case series and preclinical or experimental studies will be excluded. The quality and risk of bias will be assessed using the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk-of-Bias tool for randomised controlled trials. The primary outcome will be the proportion of critically ill patients with AKI who develop delirium. Secondary outcomes will include the proportion of patients with delirium stratified by AKI severity or receipt of renal replacement therapy as well as clinical factors associated with delirium, mortality and health service outcomes, including organ support use and lengths of stay. ETHICS AND DISSEMINATION: Ethics approval is not required for this study, as all data included in this evaluation are already published, and our study will not directly involve human participants. Findings will be disseminated through academic conferences and published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD420251001864.
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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 | 0.084 | 0.115 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".