Multi-criteria decision analysis of test endpoints for detecting the effects of endocrine active substances in fish full life cycle tests
Bibliographic record
Abstract
Fish full life cycle (FFLC) tests are increasingly required in the ecotoxicological assessment of endocrine active substances. However, FFLC tests have not been internationally standardized or validated, and it is currently unclear how such tests should best be designed to provide statistically sound and ecologically relevant results. This study describes how the technique of multi-criteria decision analysis (MCDA) was used to elicit the views of fish ecologists, aquatic ecotoxicologists and statisticians on optimal experimental designs for assessing the effects of endocrine active chemicals on fish. In MCDA qualitative criteria (that can be valued, but not quantified) and quantitative criteria can be used in a structured decision-making process. The aim of the present application of MCDA is to present a logical means of collating both data and expert opinions on the best way to focus FFLC tests on endocrine active substances. The analyses are presented to demonstrate how MCDA can be used in this context. Each of 3 workgroups focused on 1 of 3 species: fathead minnow (Pimephales promelas), Japanese medaka (Oryzias latipes), and zebrafish (Danio rerio). Test endpoints (e.g., fecundity, growth, gonadal histopathology) were scored for each species for various desirable features such as statistical power and ecological relevance, with the importance of these features determined by assigning weights to them, using a swing weighting procedure. The endpoint F1 fertilization success consistently emerged as a preferred option for all species. In addition, some endpoints scored highly in particular species, such as development of secondary sexual characteristics (fathead minnow) and sex ratio (zebrafish). Other endpoints such as hatching success ranked relatively highly and should be considered as useful endpoints to measure in tests with any of the fish species. MCDA also indicated relatively less preferred endpoints in fish life cycle tests. For example, intensive histopathology consistently ranked low, as did measurement of diagnostic biomarkers, such as vitellogenin, most likely due to the high costs of these methods or their limited ecological relevance. Life cycle tests typically do not focus on identifying toxic modes and/or mechanisms of action, but rather, single chemical concentration-response relationships for endpoints (e.g., survival, growth, reproduction) that can be translated into evaluation of risk. It is, therefore, likely to be an inefficient use of limited resources to measure these mechanism-specific endpoints in life cycle tests, unless the value of such endpoints for answering particular questions justifies their integration in specific case studies.
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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 | 0.039 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".