Criteria for the ethical conduct of psychiatric neurosurgery clinical trials
Bibliographic record
Abstract
There is an urgent need for an effective therapy for treatment-refractory mental illness. Trials ongoing globally that explore surgical treatment, such as deep brain stimulation, for refractory psychiatric disease have produced some promising early results. However, diverse inclusion criteria and variable methodological and ethical standards, combined with the sordid past of neuromodulation, confound trial interpretation and threaten the integrity of a new and emerging science. What is required is a standard of ethical practice, globally applied, for neurosurgical trials in psychiatry that protects patients and maintains a high ethical benchmark for clinicians and researchers to meet. With mental illness, as well as treatment resistance, reaching epidemic proportions, ethically and scientifically sound clinical trials will lead to effective and safe surgical treatments that will become vital components of the clinicians' armamentarium. Ethical criteria, such as the ones proposed here, need to be established now and applied in earnest if the field is to move forward and if patients with no other therapeutic options are to receive much-needed treatment.
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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 | 0.558 | 0.602 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.016 | 0.009 |
| Research integrity | 0.061 | 0.039 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".