Invasive experimental brain surgery for dementia: Ethical shifts in clinical research practices?
Bibliographic record
Abstract
The increasing dementia prevalence worldwide is driving the testing of novel therapeutic approaches, such as invasive brain technologies, despite limited clinical evidence and the risk of accelerating cognitive decline. Our manuscript (a) reviews the NIH Clinicaltrials.gov database for deep brain stimulation, stem cell implantation, and gene therapy trials on people with dementia; (b) discusses issues on beneficence, nonmaleficence, and autonomy associated with these trials; and (c) proposes nine recommendations that build on elements from the Declaration of Helsinki. We found 49 preregistered high-risk trials from nine countries planning to or involving 11,801 people with Alzheimer's or Lewy body dementia or dementia secondary to Parkinson's or Huntington's disease. Most of the people with Alzheimer's who are in these trials are from North America and East Asia. There is substantial heterogeneity in the enrolment criteria, even for trials recruiting only those with Alzheimer's disease. Although most trials enrol people in mild to moderate stages of Alzheimer's disease, trials in China enrol people who have severe Alzheimer's. Our findings highlight a pressing need to review and refine the enrolment criteria for invasive neural trials in people with dementia, considering risks, potential benefits, and capacity for informed consent. As a multidisciplinary team from Australia, the USA, Canada, and Germany with expertise in neurology, neuroscience, and ethics, we examine how it is essential to balance the risks of invasive neural research in a vulnerable population with limited capacity to provide informed consent to help advance the body of knowledge regarding a disease with limited therapeutic options.
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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 | 0.525 | 0.590 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.021 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".