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Risk of bias and problematic trials: characterising the research integrity of trials submitted to Anaesthesia.

Paul BramleyJoshua HulmanHelen Wanstall
Published in: Anaesthesia (2024)
Identification of 'problematic' trials is frequently dependent on individual patient data, which is often unavailable after publication. Additionally, there is evidence of a risk of outcome reporting bias and p-hacking in submitted trials. Implementation of alternative research and editorial practices could reduce the risk of bias and make identification of problematic trials easier.
Keyphrases
  • primary care
  • healthcare
  • case report
  • machine learning
  • deep learning
  • artificial intelligence