ArticleCancers2021
Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Exploring risk factors for endocrine-related immune-related adverse events: Insights from meta-analysis and Mendelian randomization.Human vaccines & immunotherapeutics · 2024Pooled it
- Immune checkpoint inhibitors-related thyroid dysfunction: influencing factor analysis, prediction model development, and management strategy proposal.Cancer immunology, immunotherapy : CII · 2024Pooled it
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Crosstalk Between Opioids and the Anti-Tumour Immune Checkpoint Axis.Current oncology (Toronto, Ont.) · 2026Review
- Article
- Metabolomics Reveals Potential Biomarkers for Early Detection of irAEs in ICI-Treated Patients.Drug design, development and therapy · 2026Article
- Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study.JMIR formative research · 2025Article
- Review
- Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data.JCO clinical cancer informatics · 2024Article
- Explainable Machine Learning Techniques To Predict Amiodarone-Induced Thyroid Dysfunction Risk: Multicenter, Retrospective Study With External Validation.Journal of medical Internet research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Targets of immune checkpoint inhibitors (ICIs) regulate immune homeostasis and prevent autoimmunity by downregulating immune responses and by inhibiting T cell activation. Although ICIs are widely used in immunotherapy because of their good clinical efficacy, they can also induce autoimmune-related adverse events. Thyroid-related adverse events are frequently associated with anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. The present study aims to investigate the factors associated with thyroid dysfunction in patients receiving PD-1 or PD-L1 inhibitors and to develop various machine learning approaches to predict complications. A total of 187 patients were enrolled in this study. Logistic regression analysis was conducted to investigate the association between such factors and adverse events. Various machine learning methods were used to predict thyroid-related complications. After adjusting for covariates, we found that smoking history and hypertension increase the risk of thyroid dysfunction by approximately 3.7 and 4.1 times, respectively (95% confidence intervals (CIs) 1.338-10.496 and 1.478-11.332,
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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.