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A descriptive appraisal of quality of reporting in a cohort of machine learning studies in anesthesiology
Rishi Kothari1, Catherine Chiu1, Mira Moukheiber2
1Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA 4143, USA.
Background:
The field of machine learning is being employed more and more in medicine. However, studies have shown that the quality of published studies frequently lacks completeness and adherence to published reporting guidelines. This assessment has not been done in the subspecialty of anesthesiology.
Methods:
We appraised the quality of reporting of a convenience sample of 67 peer-reviewed publications sourced from the scoping review by Hashimoto et al. Each publication was appraised on the presence of reporting elements (reporting compliance) selected from 4 peer-reviewed guidelines for reporting on machine learning studies. Results are described in several cross sections, including by section of manuscript (e.g. abstract, introduction, etc.), year of publication, impact factor of journal, and impact of publication.
Results:
On average, reporting compliance was 64% ± 13%. There was marked heterogeneity of reporting based on section of manuscript. There was a mild trend towards increased quality of reporting with increasing impact factor of journal of publication and increasing average number of citations per year since publication, and no trend regarding recency of publication.
Conclusion:
The quality of reporting of machine learning studies in anesthesiology is on par with other fields, but can benefit from improvement, especially in presenting methodology, results, and discussion points, including interpretation of models and pitfalls therein. Clinicians in today's learning health systems will benefit from skills in appraisal of evidence. Several reporting guidelines have been released, and updates to mainstream guidelines are under development, which we hope will usher in improvement in reporting quality.
