ArticleJournal of healthcare engineering2021
Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Retracted: Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-10-11Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is important to predict the potential harm to the knee joint in order to prevent football players from inflicting numerous injuries to the knee during activity. Numerous professionals have been drawn to this subject, and many viable prediction systems have been developed. Prediction of potential knee joint injury is critical to effectively avoid knee joint injury during exercise. The current prediction algorithms are mainly implemented through expert interviews, medical reports, and historical documents. The algorithms have problems with low prediction accuracy or precision values. There is a need to understand more knee injury factors and improve the prediction accuracy; hence, the intelligent prediction algorithm for potential injury of knee joints of football players is proposed in this paper. Firstly, the characteristics of the knee joint injury and the injury factors of the football players are gathered and analyzed. Then, the damage is predicted by the similarity measurement. The experimental results show that the proposed algorithm has higher prediction accuracy and shorter time. According to the findings of a survey that collected healthcare data, several key factors contribute to football knee injuries. To a degree, this algorithm can predict the likelihood of a football player's knee injury.
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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.