Impact of the COVID-19 pandemic on patients with chronic kidney disease
Bibliographic record
Abstract
ABSTRACT: Severe acute respiratory disease coronavirus 2 is currently causing the coronavirus disease 2019 (COVID-19) pandemic, placing extreme strain on the global health system. Vaccination is the main measure for preventing the COVID-19 epidemic, especially for high-risk groups including patients with chronic kidney disease (CKD). However, CKD patients receiving dialysis or kidney transplant may be characterized by decreased renal function and immune disorders, which may have uncertainties in their health. This overview aims to introduce the possible impact of the COVID-19 vaccine on kidney disease and its application in patients with CKD to provide evidence for the COVID-19 vaccine in patients with CKD. The data for this study were collected from PubMed, Cochrane Library, Embase, ClinicalTrials.gov, and the China Knowledge Resource Integrated Database (CNKI). The following keywords were used: "COVID-19", "COVID-19 vaccine," and "CKD". The publication time of the papers was set from the establishment of the databases to September 2021. A total of 47 studies were included, and patients with CKD are a high-risk group for COVID-19 infection and severe illness. Vaccination is a powerful tool for preventing CKD patients from COVID-19. Because of possible side effects, the recurrence or deterioration of kidney disease may occur in CKD patients after vaccination. Although vaccination for patients with CKD remains a problem, with the advantages outweighing the disadvantages, stable CKD patients should complete a vaccination plan, and doctors should be aware of the recurrence or deterioration of kidney disease and close monitoring. DATA ACCESS STATEMENT: Research data supporting this publication are available from the electronic databases of PubMed, Cochrane Library, Embase, ClinicalTrials.gov, and the China Knowledge Resource Integrated Database (CNKI).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".