STP Town Hall Discussion on the Use of Virtual Control Groups in Nonclinical Toxicity Studies
Bibliographic record
Abstract
The 2025 Town Hall meeting of the Society of Toxicologic Pathology (STP) discussed virtual control groups (VCG) in nonclinical toxicity testing, which are being pursued by multiple organizations to reduce animal use in product development. The current VCG literature infrequently reflects the toxicologic pathology perspective. Audience members noted that concurrent control groups (CCG) are the gold standard for toxicity studies, essential for replenishing the historical control data (HCD) from which VCG are generated. The utility of VCG is context-dependent: acceptable when test article (TA)-related effects are easily separated from background findings but likely unsuitable where subtle findings must be distinguished. Global regulatory acceptance is of paramount concern in trying to shift from CCG to partly or wholly relying on VCG. Alternative approaches to reduce animal use include eliminating studies that are a common regulatory expectation but lack scientific justification, obtaining early regulatory agreement that single-species testing is sufficient, and altering study designs to reduce group numbers, sharing CCG, or blending CCG and VCG. The most effective way for toxicologic pathologists to influence this debate is to dispassionately consider opportunities and challenges of VCG related to pathology diagnoses and interpretations and communicate this perspective clearly to the broader (non-pathologist) scientific community.
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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 | 0.070 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.048 | 0.039 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".