Toxicity Testing in the 21st Century: Implications for Human Health Risk Assessment
Bibliographic record
Abstract
At the request of the Environmental Protection Agency, the National Research Council (NRC) recently completed a major report entitled Toxicity Testing in the 21st Century: A Vision and a Strategy. The terms of reference for this report were to develop a long-range vision and strategic plan to advance the practices of toxicity testing and human health assessment of environmental agents. The report describes how current and anticipated scientific advances can be expected to transform toxicity testing to permit broader coverage of the universe of potentially toxic chemicals to which humans may be exposed, using more timely and more cost-effective methods for toxicity testing. The report envisages greatly expanded use of high- and medium-throughput in vitro screening assays, computational toxicology, and systems biology, along with other emerging high-content testing methodologies, such as functional genomics and transcriptomics. When fully implemented, the vision will transform the ways toxicity testing and chemical risk assessment are conducted, moving away from measuring apical health endpoints in experimental animals toward identification of significant perturbations of toxicity pathways using in vitro tests in human cells and cell lines. Population-based studies incorporating relevant biomarkers will also be useful in identifying pathway perturbations directly in humans and in interpreting the results of in vitro tests in the context of human health risk assessment. The present article summarizes and extends the NRC report and examines its implications for risk assessment practice.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".