Facing the Challenge of Data Transfer from Animal Models to Humans: the Case of Persistent Organohalogens
Bibliographic record
Abstract
A well-documented fact for a group of persistent, bioaccumulating organohalogens contaminants, namely polychlorinated biphenyls (PCBs), is that appropriate regulation was delayed, on average, up to 50 years. Some of the delay may be attributed to the fact that the science of toxicology was in its infancy when PCBs were introduced in 1920's. Nevertheless, even following the development of modern toxicology this story repeats itself 45 years later with polybrominated diphenyl ethers (PBDEs) another compound of concern for public health. The question is why? One possible explanation may be the low coherence between experimental studies of toxic effects in animal models and human studies. To explore this further, we reviewed a total of 807 PubMed abstracts and full texts reporting studies of toxic effects of PCB and PBDE in animal models. Our analysis documents that human epidemiological studies of PBDE stand to gain little from animal studies due to the following: 1) the significant delay between the commercialisation of a substance and studies with animal models; 2) experimental exposure levels in animals are several orders of magnitude higher than exposures in the general human population; 3) the limited set of evidence-based endocrine endpoints; 4) the traditional testing sequence (adult animals--neonates--foetuses) postpones investigation of the critical developmental stages; 5) limited number of animal species with human-like toxicokinetics, physiology of development and pregnancy; 6) lack of suitable experimental outcomes for the purpose of epidemiological studies. Our comparison of published PCB and PBDE studies underscore an important shortcoming: history has, unfortunately, repeated itself. Broadening the crosstalk between the various branches of toxicology should therefore accelerate accumulation of data to enable timely and appropriate regulatory action.
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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 | 0.395 | 0.637 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".