Assessing the impact of screening, early identification and intervention programmes for chronic kidney disease: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Chronic kidney disease (CKD) is a major threat to public health, especially in low-income and lower middle-income countries, where resources for treating patients with advanced CKD are scarce. Although early CKD identification and intervention hold promise for reducing the burden of CKD and risk factors, it remains unclear if an uniform strategy can be applicable across all income groups. The aim of this scoping review is to synthesise available evidence on early CKD identification programmes in all world regions and income groups. The study will also identify efforts that have been made to use interventions and implementation of early identification programmes for CKD across countries and income groups. METHODS AND ANALYSIS: This review will be guided by the methodological framework for conducting scoping studies developed by Arksey and O'Malley. Empirical (Medline, Embase, Cochrane Library, CINAHL, ISI Web of Science and PsycINFO) and grey literature references will be searched to identify studies on CKD screening, early identification and interventions across all populations. Two reviewers will independently screen references in consecutive stages of title/abstract screening and then full-text screening. We will use a general descriptive overview, tabular summaries and content analysis on extracted data. ETHICS AND DISSEMINATION: The findings from our planned scoping review will enable us to identify items in early identification programmes that can be used in developing screening toolkits for CKD. We will disseminate our findings using traditional approaches that include open-access peer-reviewed publication, scientific presentations and a white paper (call to action) report. Ethical approval will not be required for this scoping review as the data will be extracted from already published studies.
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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 | 0.123 | 0.115 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.083 | 0.017 |
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".