Quantitative and qualitative concordance between clinical and nonclinical toxicity data
Bibliographic record
Abstract
Although rodent toxicity testing plays an important role in evaluating human hazards of environmental and industrial chemicals, evaluating the concordance of the rodent testing results with human effects is challenging because these chemicals cannot be tested in humans. In this study, we evaluate the quantitative and qualitative concordance of lowest observed adverse effect levels (LOAELs) and adverse endpoints between in vivo and in vitro models of human health and human clinical trials of pharmaceuticals. Rodent human equivalent dose-adjusted LOAEL (LOAELHED) values and human LOAEL values for the sensitive effect in each species were moderately correlated in a protective context. When matched rodent and human effects were evaluated, the quantitative correlation in dose did not improve, and the qualitative balanced accuracy in effects was low, suggesting limited predictivity. Absolute differences in rodent LOAELHED and human LOAEL values were nearly 1 log10 unit with rodent LOAELHED values consistently higher; however, rodent LOAELHED values were less than the human LOAEL values for >95% of drugs when divided by typical composite uncertainty factors. In comparison, in vitro bioactivity administered equivalent dose (AED) values showed a similar moderate correlation and absolute differences with human LOAEL values, but in vitro bioactivity AED values were consistently lower. When in vitro bioactivity AED values were compared with rodent LOAELHED values, the correlation was lower and differences larger relative to human LOAEL comparison. Overall, the study expands previous efforts evaluating the concordance of rodent toxicological testing results with human responses and presents objective expectations for alternative toxicity testing approaches.
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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 | 0.061 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".