PERSONAL IDENTITY, ENHANCEMENT AND NEUROSURGERY: A QUALITATIVE STUDY IN APPLIED NEUROETHICS
Bibliographic record
Abstract
Recent developments in the field of neurosurgery, specifically those dealing with the modification of mood and affect as part of psychiatric disease, have led some researchers to discuss the ethical implications of surgery to alter personality and personal identity. As knowledge and technology advance, discussions of surgery to alter undesirable traits, or possibly the enhancement of normal traits, will play an increasingly larger role in the ethical literature. So far, identity and enhancement have yet to be explored in a neurosurgical context, despite the fact that 1) neurological disease and treatment both potentially alter identity, and 2) that neurosurgeons will likely be the purveyors of future enhancement implantable technology. Here, we use interviews with neurosurgical patients to shed light on the ethical issues and challenges that surround identity and enhancement in neurosurgery. The results provide insight into how patients approach their identity prior to potentially identity-altering procedures and what future ethical challenges lay ahead for clinicians and researchers in the field of neurotherapeutics.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".