Emerging Neurotechnologies: Implications for Professional Relations and Communication
Bibliographic record
Abstract
Rapid advances in neurotechnology and neurosurgery are positioned to revolutionize care for patients suffering from debilitating neurological and psychiatric disease. Enthusiasm for the adoption of these technologies is tempered by ethical dilemmas regarding resource allocation, provision of care, communication with patients and other providers, and other potential pitfalls. In the present work, we discuss bioethical implications of novel neurotechnologies for medical practice. In particular, we examine the implications of neurotechnological advancement through the lens of professional communication. Emerging challenges within this domain are presented in the context of physician interactions with four key partners: (i) patients; (ii) other physicians; (iii) industry; and (iv) society-at-large. Anticipated issues as well as mitigation strategies are discussed as they relate to communication with these stakeholders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".