The Structure of Clinical Translation: <i>Efficiency, Information, and Ethics</i>
Bibliographic record
Abstract
Abstract The last two decades have witnessed a crescendo of allegations that clinical translation is rife with waste and inefficiency. Patient advocates argue that excessively demanding regulations delay access to life‐saving drugs, research funders claim that too much basic science languishes in academic laboratories, journal editors allege that biased reporting squanders public investment in biomedical research, and drug companies (and their critics) argue that far too much is expended in pharmaceutical development . But how should stakeholders evaluate the efficiency of translation and proposed reforms to drug development? Effective reforms require an accurate model of the systems they aspire to improve—their components, their proper functions, and their pathologies. However, there is currently no explicit and well‐developed model of translation for evaluating such criticisms . In what follows, we offer an explicit model of clinical translation. Many discussions of clinical translation and its pathologies presume that its main output is tangible: new drugs, vaccines, devices, and diagnostics. We disagree. We argue that the principal output of clinical translation is information—in particular, information about the coordinated set of materials, practices, and constraints needed to safely unlock the therapeutic or preventive activities of drugs, biologics, and diagnostics. To develop this information calls for a process far different from a simple linear progression of clinical trials; it requires exploratory sampling of many different elements in this set. Our model points to some limitations and liabilities of influential proposals for reforming research. It also reveals some underrecognized opportunities for improving the efficiency of clinical translation .
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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 | 0.110 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.110 |
| Scholarly communication | 0.029 | 0.030 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".