Launching invasive, first‐in‐human trials against Parkinson's disease: Ethical considerations
Bibliographic record
Abstract
The decision to initiate invasive, first-in-human trials involving Parkinson's disease presents a vexing ethical challenge. Such studies present significant surgical risks, and high degrees of uncertainty about intervention risks and biological effects. We argue that maintaining a favorable risk-benefit balance in such circumstances requires a higher than usual degree of confidence that protocols will lead to significant direct and/or social benefits. One critical way of promoting such confidence is through the application of stringent evidentiary standards for preclinical studies. We close with a series of recommendations for strengthening the internal and external validity of preclinical studies, reducing their tendency toward optimism and publication biases, and improving the knowledge base used to design and evaluate preclinical studies.
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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 | 0.555 | 0.527 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.032 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".