ArticleHeliyon2024
Predicting immunogenicity of COVID-19 vaccines in hemodialysis patients with renal disease.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 citations in OpenAlex.
- Total immunoglobulin G concentration as a correlate of anemia in dialysis patients: machine learning analysis of clinical and serological factors.Frontiers in immunology · 2026Article
- The Impact of Hemodialysis on Humoral and Cellular Immunity in Patients with Renal Failure.Journal of clinical medicine · 2025Article
- Silent reactivation of Varicella Zoster virus in hemodialysis patients.: A management dilemma.Saudi medical journal · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Individuals who are diagnosed with chronic kidney disease, particularly those receiving maintenance hemodialysis treatment, face a greater likelihood of suffering from severe symptoms and fatality due to COVID-19. This study aimed to explore the optimal vaccination approach for these individuals. The study used data analysis tasks such as data preprocessing, cleaning, and exploration, and machine learning models including linear regression, random forest, XGBoost, gradient boosting, AdaBoost, decision trees, Lasso, and ridge regression were used to construct the predictive model. The study found that the Lasso model performed the best overall in predicting anti-S IgG antibodies levels in response to COVID-19 vaccines for people with kidney failure with MAE of 8.81, RMSE of 19.59, and R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.