Readiness for First-In-Human Neuromodulatory Interventions
Bibliographic record
Abstract
BACKGROUND: Novel neurointerventions present innovative therapeutic approaches to a range of treatment-refractory disorders. We sought to characterize factors that inform and define translational readiness for first-in-human (FIH) neuromodulatory trials. METHODS: We used a two-part methodology involving a scoping review of the biomedical literature on the readiness of FIH trials for both neurological and non-neurological applications, and semi-structured interviews with stakeholders about decision-making for neuromodulation using magnetic resonance-guided focused ultrasound as a case example. RESULTS: One hundred and thirty factors relevant to FIH readiness were identified in the scoping review. Trial design, adequacy of preclinical evidence, and risk were ubiquitous across biotechnologies. Target organ, target function, and inadequacy of animal models were dominant in the neurointervention literature. Interview results on the relative importance of these factors reveal divergent values, priorities, and understandings both between patients and clinicians and between patients affected by different conditions. CONCLUSION: Readiness of neurotechnology for FIH trials is defined by a multitude of interacting factors that pertain to clinical and nonclinical priorities, perceptions, and values.
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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 | 0.256 | 0.432 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".