Limitations to Empirical Extrapolation Studies: The Case of BMD Ratios
Bibliographic record
Abstract
Extrapolation relationships are of keen interest to chemical risk assessment in which they play a prominent role in translating experimentally derived (usually in animals) toxicity estimates into estimates more relevant to human populations. A standard approach for characterizing each extrapolation relies on ratios of pre-existing toxicity estimates. Applications of this "ratio approach" have overlooked several sources of error. This article examines the case of ratios of benchmark doses, trying to better understand their informativeness. The approach involves mathematically modeling the process by which the ratios are generated in practice. Both closed form and simulation-based models of this "data-generating process" (DGP) are developed, paying special attention to the influence of experimental design. The results show the potential for significant limits to informativeness, and revealing dependencies. Future applications of the ratio approach should take imprecision and bias into account. Bootstrap techniques are recommended for gauging imprecision, but more complicated techniques will be required for gauging bias (and capturing dependencies). Strategies for mitigating the errors are suggested.
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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 | 0.384 | 0.763 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".