#63: <b>Mark McAuley & Susan Boehnke – Patient-led Research, Patient-Centered Care and Neuroscience Education</b>
Bibliographic record
Abstract
In this episode, we have the pleasure of hosting Mark McAuley, a deep brain stimulation (DBS) patient and the CEO of Astronomy Australia Limited, and Dr. Susan Boehnke, an Associate Professor at Queen’s University in Canada and director of the Neurotech Microcredential Program and the Neurotech Discovery Lab. Together, they've been part of remarkable efforts that not only focus on the practical and ethical aspects of neurotechnology but also engage students in real-time research.Mark brings over thirty years of experience in research and development, with a remarkable track record of securing $300 million in Australian Government grants for major research infrastructure projects. Holding degrees in astrophysics, ancient history, and an MBA—where he was awarded the Vice-Chancellor’s medal as the university's highest-achieving postgraduate student—Mark was diagnosed with Parkinson’s disease in 2010 and received a DBS implant in 2020. Post-surgery, he's been a passionate advocate for better patient care and improved DBS programming to enhance clinical outcomes.Dr. Susan Boehnke completed her PhD in Neuroscience at Dalhousie University and has an extensive background in auditory neuroscience and primate neurophysiology. She led the creation of one of the first non-human primate models of Alzheimer’s disease and established Canada's first non-human primate biobank. In response to the explosion of interest in neurotechnology, she's now pioneering a micro-credential program in neurotech and exploring the ethical issues surrounding it. She's also leading the Training Committee for Connected Minds, a significant research initiative between York and Queen’s Universities.At the recent Neuromodec conference in New York, Mark and Susan presented their work, which emphasizes patient inclusion in research—a theme Dr. Boehnke passionately advocates for—and provides transformative learning experiences for students. Today, we'll dive into their journeys, the science behind their projects, and explore potential insights into the future of patient-led research and education in neurotechnology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.185 | 0.134 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".