ArticleFrontiers in public health2025
Exploring the relationship between per- and polyfluoroalkyl substances exposure and rheumatoid arthritis risk using interpretable machine learning.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Short-Term Machine-Learning Calibration of PID Sensors for Ambient VOC OH Reactivity.Sensors (Basel, Switzerland) · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rheumatoid arthritis is a chronic autoimmune disease influenced by environmental exposures, including per- and polyfluoroalkyl substances (PFAS). Although previous studies have suggested links between PFAS and rheumatoid arthritis risk, none have used interpretable machine learning models for prediction. This study aimed to develop such a model to assess risk based on PFAS exposure. Methods: We analyzed data from 11,705 participants in the National Health and Nutrition Examination Survey (2003-2018). Twelve machine learning algorithms were evaluated using metrics including area under the curve (AUC), accuracy, sensitivity, specificity, and F Results: CatBoost showed the best performance (AUC: 0.82; Accuracy: 74%; F Conclusions: This study demonstrates the potential of machine learning to predict rheumatoid arthritis risk based on PFAS exposure. The identified non-linear patterns provide insights into environmental contributions to disease risk and may inform future prevention strategies.
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.