Deep Brain Stimulation and the Search for Identity
Bibliographic record
Abstract
Ethical evaluation of deep brain stimulation as a treatment for Parkinson's disease is complicated by results that can be described as involving changes in the patient's identity. The risk of becoming another person following surgery is alarming for patients, caregivers and clinicians alike. It is one of the most urgent conceptual and ethical problems facing deep brain stimulation in Parkinson's disease at this time. In our paper we take issue with this problem on two accounts. First, we elucidate what is meant by "becoming another person" from a conceptual point of view. After critically discussing two broad approaches we concentrate on the notion of "individual identity" which centers on the idea of "core attitudes". Subsequently we discuss several approaches to determine what distinguishes core attitudes from those that are more peripheral. We argue for a "foundational-function model" highlighting the importance of specific dependency relations between these attitudes. Our second aim is to comment on the possibility to empirically measure changes in individual identity and argue that many of the instruments now commonly used in selecting and monitoring DBS-patients are inappropriate for this purpose. Future research in this area is advised combining a conceptual and an empirical approach as a basis of sound ethical appraisal.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".