Metascience Matters
Alexander Gibson is a PhD student at the Queensland University of Technology and the Australian Centre for Health Services Innovation studying the intersection of metascience and clinical machine learning. One of his focus areas is data provenance, the Who, What, Where, When, Why, and How of datasets, and how neglecting this can lead to bad outcomes in medical machine learning not only in research, but also for clinical practice and medical device approval. CONTACT RANDY: Feedback: metasciencematters@gmail.com EPISODE LINKS: Alex's preprint on unreliable diabetes and stroke datasets: https://www.medrxiv.org/content/10.64898/2026.02.24.26347028v2 OUTLINE: 0:00 - Introduction 3:14 - The beginning of Alex's interest in clinical predictive modeling 5:05 - Alex's interest in metascience 6:42 - Choosing a dissertation topic/metrics hacking in machine learning 9:49 - Preprint on data provenance in medical datasets 12:33 - The diabetes and stroke datasets Alex investigated 16:46 - Major irregularities in the data 23:29 - TRIPOD+AI guidelines for auditing machine learning studies 25:26 - How unreliable studies can impact clinical practice and medical device patents 26:42 - Citation networks 27:37 - AI-generated formulaic medical machine learning studies 31:50 - Strategies for high-quality data provenance 33:53 - Patents citing unreliable studies, and how to integrate data provenance into peer review 35:23 - The biggest problems for clinical predictive modeling studies 37:02 - Resources and tools for improving rigor in machine learning 38:45 - Metrics reporting 40:45 - Choosing decision thresholds in predictive models 42:59 - The importance of clinical context in metrics reporting 45:21 - The unreasonable effectiveness of age and sex as predictors 47:53 - The roles of academia and industry in improving clinical machine learning studies 50:07 - Explanation versus prediction 52:51 - Advice and resources for students 54:27 - Outro
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