Informed consent for clinical trials of deep brain stimulation in psychiatric disease: challenges and implications for trial design: Table 1
Bibliographic record
Abstract
Advances in neuromodulation and an improved understanding of the anatomy and circuitry of psychopathology have led to a resurgence of interest in surgery for psychiatric disease. Clinical trials exploring deep brain stimulation (DBS), a focally targeted, adjustable and reversible form of neurosurgery, are being developed to address the use of this technology in highly selected patient populations. Psychiatric patients deemed eligible for surgical intervention, such as DBS, typically meet stringent inclusion criteria, including demonstrated severity, chronicity and a failure of conventional therapy. Although a humanitarian device exemption by the US Food and Drug Administration exists for its use in obsessive-compulsive disorder, DBS remains a largely experimental treatment in the psychiatric context, with its use currently limited to clinical trials and investigative studies. The combination of a patient population at the limits of conventional therapy and a novel technology in a new indication poses interesting challenges to the informed consent process as it relates to clinical trial enrollment. These challenges can be divided into those that relate to the patient, their disease and the technology, with each illustrating how traditional conceptualisations of research consent may be inadequate in the surgical psychiatry context. With specific reference to risk analysis, patient autonomy, voluntariness and the duty of the clinician-researcher, this paper will discuss the unique challenges that clinical trials of surgery for refractory psychiatric disease present to the consent process. Recommendations are also made for an ethical approach to clinical trial consent acquisition in this unique patient population.
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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 | 0.379 | 0.418 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 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; 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".