Neuroethics and Psychiatry: New Collaborations for Emerging Challenges
Bibliographic record
Abstract
“Better sick than lazy,” wrote one respondent on a survey assessing receptivity to diagnostic brain scans for depression. Mental illness, long the domain of psychiatry, may prove to be the first major medical or scientific area to intrude on the average sense of “intuitive dualism” — the conviction that the mind, which can be judged to be lazy, is treated as distinct from the brain, which may be broken when sick. Widespread information about brain scans and promise of targeted new neurotreatments may bring more untreated mental illness into the clinic and increase treatment compliance, if the allure of anything neuro-based helps biologize mental illness and treatments become more tolerable with fewer side effects. Basic and clinical neuroscience research resulting in new technologies and drugs to manipulate the brain and relieve or cure mental illness will compel this merger of psychiatry, neurology, and neurosurgery. These developments herald an era of great hope for patients who may return to a normal life and for practitioners who will have access to more tools for effective diagnosis and treatment. ABOUT THE AUTHORS Emily R. Murphy, PhD, is a Fellow, Center for Law and the Biosciences, Stanford Law School. Judy Illes, PhD, is Professor of Neurology and Canada Research Chair in Neuroethics, National Core for Neuroethics, University of British Columbia, Vancouver, British Columbia. Address correspondence to: Emily R. Murphy, PhD, Center for Law and the Biosciences, 559 Nathan Abbott Way, Stanford, CA 94305-8610; or e-mail ermurphy@ stanford.edu. Dr. Murphy and Dr. Illes have disclosed no relevant financial relationships. EDUCATIONAL OBJECTIVES Describe the scope of the field of neuroethics. Discuss the ethical implications of newer technologies applied to psychiatry. Review potential ethical issues raised by the prospect of neurochemical manipulation of memory and cognition.
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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 | 0.121 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.015 | 0.066 |
| Scholarly communication | 0.025 | 0.069 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.037 | 0.059 |
| Insufficient payload (model declined to judge) | 0.041 | 0.007 |
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".