Building a bridge between animal studies and human randomized trials
Bibliographic record
Abstract
Animal experiments are widely used in biomedical research to develop new therapies for human disease. Such experiments are conducted under the assumption that animal models mirror human disease with a high degree of fidelity and that findings of efficacy in animal models will generally translate into effective human interventions. Yet experience shows that many agents which prevent or treat disease in animal models fail to replicate when the results are tested in subsequent human clinical trials. The problem is evident in many domains but is especially apparent in cardiovascular medicine. This dissertation formulates a comprehensive strategy based on health services research to evaluate the results of animal studies before clinical trials are undertaken. The strategy is applied to address three areas in vascular medicine where promising animal data support therapeutic efficacy. The results suggest this approach yields a number of advantages over the current pathway of taking animal data directly into clinical trials. First, by imposing an intermediate phase between animal experimentation and randomized trials, health services research can be used to vet animal findings for human applicability and relevance; such work can act as a stage of sober, second thought. Second, health services studies can generate information on effect size and other vital variables for planning successful clinical trials. Third, health services studies can shed light on many elements not often seen in animal data, such as toxicity, cost-effectiveness, the impact of comorbidity, and evaluation of class effects and non-compliance. Fourth, the approach can be adapted to translating animal evidence on etiology and pathogenesis to human beings. Ultimately, the most significant implication of this approach is to enhance the process by which animal data is transformed into advances in the prevention and treatment of human disease.
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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 | 0.749 | 0.780 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.011 | 0.022 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".