Economic burden of acute kidney injury in children and adults: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Acute kidney injury (AKI) is common in hospitalised adults and children and is associated with significantly increased mortality and worse short-term and long-term outcomes. This systematic review and meta-analysis will evaluate the cost associated with AKI. METHODS AND ANALYSIS: This health economic analysis will be performed using systematic search of databases, including MEDLINE, EMBASE, CINAHL, Scopus and Cochrane Library from 2009 to the present (search completed on 27 May 2024). Two reviewers will independently complete study selection, data extraction and bias assessment. Inclusion criteria will be randomised controlled trials (RCTs) and observational studies (cohort or case-control) from all countries of hospitalised adults and children. The exposure will be AKI based on definitions using serum creatinine and/or urine output criteria, relative to patients without AKI. The primary outcome studied will be the cost of index hospitalisation associated with AKI episode. Other secondary outcomes will include the cost of intensive care unit admission during index hospitalisation, direct costs related to inpatient and outpatient care) and indirect (time) costs related to loss of productivity. Pooled random-effect meta-analysis ORs with 95% CIs will be reported. ETHICS AND DISSEMINATION: Ethics approval was not required due to study methodology. The authors have no competing interests to report. The results will be disseminated in peer-reviewed publications according to guidelines by the Cochrane and Centre for Reviews and Dissemination. PROSPERO REGISTRATION NUMBER: CRD42024512658.
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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 | 0.067 | 0.111 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.005 |
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".