A Systematic Review to Compare Chemical Hazard Predictions of the Zebrafish Embryotoxicity Test With Mammalian Prenatal Developmental Toxicity
Bibliographic record
Abstract
Originally developed to inform the acute toxicity of chemicals on fish, the zebrafish embryotoxicity test (ZET) has also been proposed for assessing the prenatal developmental toxicity of chemicals, potentially replacing mammalian studies. Although extensively evaluated in primary studies, a comprehensive review summarizing the available evidence for the ZET's capacity is lacking. Therefore, we conducted a systematic review of how well the presence or absence of exposure-related findings in the ZET predicts prenatal development toxicity in studies with rats and rabbits. A two-tiered systematic review of the developmental toxicity literature was performed, a review of the ZET literature was followed by one of the mammalian literature. Data were extracted using DistillerSR, and study validity was assessed with an amended SYRCLE's risk-of-bias tool. Extracted data were analyzed for each species and substance, which provided the basis for comparing the 2 test methods. Although limited by the number of 24 included chemicals, our results suggest that the ZET has potential to identify chemicals that are mammalian prenatal developmental toxicants, with a tendency for overprediction. Furthermore, our analysis confirmed the need for further standardization of the ZET. In addition, we identified contextual and methodological challenges in the application of systematic review approaches to toxicological questions. One key to overcoming these challenges is a transition to more comprehensive and transparent planning, conduct and reporting of toxicological studies. The first step toward bringing about this change is to create broad awareness in the toxicological community of the need for and benefits of more evidence-based 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 | 0.038 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".