Our evolving understanding of placebo effects: implications for research and practice in neuropsychiatry
Bibliographic record
Abstract
Dr. Matthew Burke is a Cognitive Neurologist in the Neuropsychiatry Program and Department of Psychiatry at Sunnybrook Health Sciences Center. He is currently the Director of Sunnybrook’s Traumatic Brain Injury Clinic and he also sees patients with functional neurological disorders, headache disorders, and other neuropsychiatric conditions. Before starting at Sunnybrook, Dr. Burke completed medical school and his neurology residency training at the University of Toronto, where he was the Chief Neurology Resident in his last year of training. He then completed a two-year Sidney R. Baer, Jr. Foundation fellowship in Cognitive Neurology and Neuropsychiatry at Beth Israel Deaconess Medical Center and Harvard Medical School. Dr. Burke’s clinical research fellowship provided specialized training in non-invasive brain stimulation and brain network mapping. His research applies these novel techniques to investigate the complex and poorly understood brain disorders at the interface between neurology and psychiatry. He also has research interests in the neurobiology of placebo effects and is an active collaborator with the Harvard Program in Placebo Studies. Finally, concurrent with his fellowship, Dr. Burke completed the Harvard Catalyst Clinical Translational Research Academy. This is a NIH-funded program that provides advanced training in methods of clinical investigation. Dr. Burke’s research to date has resulted in multiple peer-reviewed publications, media attention on platforms such as CNN and BBC, and recent recognition with the American Neuropsychiatric Association’s 2019 Young Investigator Award and the American Headache Society’s 2018 Frontiers in Headache Research Award. Abstract Placebo effects are the beneficial therapeutic effects derived from the context surrounding the administration of a treatment rather than the treatment itself. Recent research has shifted our understanding of placebo effects from a mystical unempirical entity to a biologically-based phenomenon capable of meaningfully modulating brain regions and neurotransmitter systems. In this presentation, Dr. Burke will begin by summarizing the evidence underlying the principles and neurobiology of placebo effects. He will then discuss clinical factors that contribute to placebo effects and how placebo effects could be harnessed in the management of neuropsychiatric disorders. Finally, Dr. Burke will interrogate how our evolving understanding of placebo effects may impact the way we design, appraise and interpret research studies in neuropsychiatry and across medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.215 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.016 | 0.038 |
| Open science | 0.011 | 0.009 |
| Research integrity | 0.017 | 0.032 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".