ArticleComputational intelligence and neuroscience2022
Sports Injury Identification Method Based on Machine Learning Model.
Article in Computational intelligence and neuroscience, 2022. 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 It has been retracted, and should not be counted.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
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
1 citing paper in PubMed, 11 citations in OpenAlex.
- Retracted: Sports Injury Identification Method Based on Machine Learning Model.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-08-02Concerns/Issues about Data · Concerns/Issues about Human Subject Welfare · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/39F4746EE7D24C2DB104371FB13B24
- Retracted
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the increasingly fierce competition in international competitive sports, the momentum of special training has increased. Sports injuries are becoming more and more serious, which restricts the further improvement of the level of athletes. How to solve the problem of prevention, treatment, and rehabilitation of sports injuries, so as to ensure the normal training and competition of athletes, is an important part of sports work. Machine learning can solve large-scale data problems that cannot be solved by human beings at present and has strong self-learning ability, self-optimization ability, and strong generalization ability. Therefore, the purpose of this study is to understand the characteristics of rhythmic gymnastics injuries and analyze their causes by investigating the injury status of elite rhythmic gymnasts. According to the characteristics of the project, the injury characteristics of the athletes themselves, and other factors, using scientific qualitative and quantitative indicators, the injury risk of key athletes in rhythmic gymnastics was evaluated. It also provides theoretical and practical references for preventing sports injuries, formulating and implementing sports injury rehabilitation programs. The experimental results show that the female vaulting risk in the five risk categories fluctuates from 179.62 to 365.8, ranking the first in the risk of acute sports injury.
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.