A theoretical framework for early human studies: uncertainty, intervention ensembles, and boundaries
Bibliographic record
Abstract
Clinical development of novel therapeutics begins with a coordinated sequence of early phase clinical trials. Such early human studies confront a series of methodological and ethical challenges. In what follows, I propose a theoretical framework for early human studies aimed at informing the negotiation of these challenges. At the outset of clinical development, researchers confront a virtually undifferentiated landscape of uncertainty with respect to three variables: outcomes, their probability of occurrence, and operation dimensions needed to effectuate favorable outcomes. Early human trials transform this uncertain landscape into one where there are grounds for belief about risk and benefit for various combined operation dimensions. To accomplish this, studies set out with two aims. First, they identify a set of operation dimensions that, when combined as a package (intervention ensemble), elicits a reasonable probability of a target outcome. Second, they define the boundaries of dimension values within an intervention ensemble. This latter aim entails exposing at least some volunteers in early studies to treatments that are inactive or excessive. I provide examples that illustrate the way early human studies discover and delimit intervention ensembles, and close by offering some implications of this framework for ethics, methodology, and efficiency in clinical development of new interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.406 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.027 | 0.010 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads 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".