Effectiveness of vaccination in patients undergoing dialysis or patients with chronic kidney disease: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Vaccination has been an effective public health intervention for immunising individuals against many common communicable and non-communicable diseases. However, there is limited information on the efficacy of vaccination among patients undergoing dialysis or patients with chronic kidney disease (CKD). The objective of this review is to assess the effectiveness of vaccination within dialysis and CKD patient populations. METHODS AND ANALYSIS: This will be a systematic review of studies assessing the effectiveness of vaccination among CKD and dialysis patients. Relevant studies will be identified using MEDLINE, Embase, Scopus and Cochrane Library. All searches will be conducted from database inception to October 2025. Only observational studies such as cohort, prospective, retrospective and cross-sectional studies will be included. Data pertaining to patient outcomes and study design will be extracted. A narrative synthesis will be conducted as well as a meta-analysis if data permitting this analysis is extracted from included studies. ETHICS AND DISSEMINATION: Since data collection will be conducted by examining existing studies, no ethical approval or consent will be required. The results of this review will be published in a peer-reviewed journal as well as presented at seminars, conferences and symposiums. TRIAL REGISTRATION NUMBER: This review protocol has been registered in International Prospective Register of Systematic Reviews (PROSPERO), registration number CRD42025648534.
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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 | 0.063 | 0.058 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.015 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 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".