Assay Sensitivity and the Epistemic Contexts of Clinical Trials
Bibliographic record
Abstract
This article examines the concept of assay sensitivity in clinical research. Defined as the ability of a clinical trial to distinguish between an effective and ineffective treatment, the need for assay sensitivity has been taken to support the claim that placebos are methodologically superior to active control treatments. The demands of doing good clinical science must trump the physician-researcher's ethical duty to provide all trial participants with nothing less than competent medical care. We argue that this supposed implication of assay sensitivity rests on (1) collapsing the distinction between biological efficacy and clinical effectiveness, and (2) conflating the epistemic contexts of a trial-as-designed and a trial-as-executed. Once these errors are corrected, it becomes clear that placebos grant no epistemological advantage over active controls, and there is therefore no longer a tension between the epistemic and ethical demands of research. We suggest that the legitimate worries behind assay sensitivity can be better understood as the need for researchers to articulate their experimental heuristics and to demonstrate a robust pattern of evidence across a series of trials.
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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 | 0.322 | 0.468 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.116 |
| Scholarly communication | 0.017 | 0.031 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".