In Global Health Research, Is It Legitimate To Stop Clinical Trials Early on Account of Their Opportunity Costs?
Bibliographic record
Abstract
BACKGROUND TO THE DEBATE: After the failure of three large clinical trials of vaginal microbicides, a Nature editorial stated that the microbicide field "requires a mechanism to help it make rational choices about the best candidates to move through trials" [1]. In this month's debate, James Lavery and colleagues propose a new mechanism, based on stopping trials early for "opportunity costs." They argue that microbicide trial sites could have been saturated with trials of scientifically less advanced products, while newer, and potentially more promising, products were being developed. They propose a mechanism to reallocate resources invested in existing trials of older products that might be better invested in more scientifically advanced products that are awaiting clinical testing. But David Buchanan argues that the early stopping of trials for such opportunity costs would face insurmountable practical barriers, and would risk causing harm to the participants in the trial that was stopped.
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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 | 0.260 | 0.415 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.061 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.050 | 0.039 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".