ArticleTribology international2019
Predicting the polyethylene wear rate in pin-on-disc experiments in the context of prosthetic hip implants: deriving a data-driven model using machine learning methods.
Article in Tribology international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed, 60 citations in OpenAlex.
- Machine Learning and Artificial Intelligence in Metallic Orthopedic Implant Development: A Narrative Review.Materials (Basel, Switzerland) · 2026Review
- Wear Prediction of Functionally Graded Composites Using Machine Learning.Materials (Basel, Switzerland) · 2024Article
- Prediction of Tribological Properties of UHMWPE/SiC Polymer Composites Using Machine Learning Techniques.Polymers · 2023Article
- Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.Medical & biological engineering & computing · 2023Review
- Practice of Simulation and Life Cycle Assessment in Tribology-A Review.Materials (Basel, Switzerland) · 2020Review
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 1 institution in 1 country.
Funding
Abstract
Pin-on-disc (PoD) experiments are widely used to quantify and rank wear of different material couples for prosthetic hip implant bearings. However, polyethylene wear results obtained from different PoD experiments are sometimes difficult to compare, which potentially leaves information inaccessible. We use machine learning methods to implement several data-driven models, and subsequently validate them by quantifying the prediction error with respect to published experimental data. A data-driven model can supplement results from PoD wear experiments, and enables predicting polyethylene wear of new PoD experiments based on its operating parameters. It also reveals the relative contribution of individual PoD operating parameters to the resulting polyethylene wear, thus informing design of experiments, and potentially reducing the need for time consuming PoD wear measurements.
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.