Global trends in chronic kidney disease-related mortality: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: In recent decades, all-cause mortality has increased among individuals with chronic kidney disease (CKD), influenced by factors such as aetiology, standards of care and access to kidney replacement therapies (dialysis and transplantation). The recent COVID-19 pandemic also affected mortality over the past few years. Here, we outline the protocol for a systematic review to investigate global temporal trends in all-cause mortality among patients with CKD at any stage from 1990 to current. We also aim to assess temporal trends in the mortality rate associated with the COVID-19 pandemic. METHODS AND ANALYSIS: We will conduct a systematic review of studies reporting mortality for patients with CKD following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We will search electronic databases, national and multiregional kidney registries and grey literature to identify observational studies that reported on mortality associated with any cause for patients with CKD of all ages with any stage of the disease. We will collect data between April and August 2023 to include all studies published from 1990 to August 2023. There will be no language restriction, and clinical trials will be excluded. Primary outcome will be temporal trends in CKD-related mortality. Secondary outcomes include assessing mortality differences before and during the COVID-19 pandemic, exploring causes of death and examining trends across CKD stages, country classifications, income levels and demographics. ETHICS AND DISSEMINATION: A systematic review will analyse existing data from previously published studies and have no direct involvement with patient data. Thus, ethical approval is not required. Our findings will be published in an open-access peer-reviewed journal and presented at scientific conferences. PROSPERO REGISTRATION NUMBER: CRD42023416084.
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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 | 0.077 | 0.085 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.094 | 0.012 |
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".