ArticleJournal of healthcare engineering2021
The Rehabilitation Training Simulation of High Difficulty Movement and Sports Strain Site Based on Big Data.
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 It has been retracted, and should not be counted.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
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.
- Retracted: The Rehabilitation Training Simulation of High Difficulty Movement and Sports Strain Site Based on Big Data.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-07-12Concerns/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· Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We study the rehabilitation training of damaged parts of ice and snow sports clock and ensure the physical safety of athletes. The results show that the RBF neural network updates the center, weight, and width of the radial basis function, and the predicted maximum compliance is 99%, and the minimum compliance is 93%. After many analysis times, the prediction results show that the difference between the predicted degree of conformity and the actual results is less than 8%. The RBF neural network is trained according to the risk database of sports injury, and the RBF neural network will output corresponding values to realize sports injury estimation. The experimental results show that the designed model has high precision and efficiency.
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.