Integrating epidemiology and toxicology in neurotoxicity risk assessment
Bibliographic record
Abstract
Neurotoxicity risk assessments depend on the best available scientific information, including data from animal toxicity studies, human experimental studies and human epidemiology studies. There are several factors to consider when evaluating the comparability of data from studies. Regarding the epidemiology literature, issues include choice of study design, use of appropriate controls, methods of exposure assessment, subjective or objective evaluation of neurological status, and assessment and statistical control of potential confounding factors, including co-exposure to other agents. Animal experiments must be evaluated regarding factors such as dose level and duration, procedures used to assess neurological or behavioural status, and appropriateness of inference from the animal model to human neurotoxicity. Major factors that may explain apparent differences between animal and human studies include: animal neurological status may be evaluated with different procedures than those used in humans; animal studies may involve shorter exposure durations and higher dose levels; and most animal studies evaluate a single substance whereas humans typically are exposed to multiple agents. The comparability of measured outcomes in animals and humans may be improved by considering functional domains rather than individual test measures. The application of predictive models, weight of evidence considerations and meta-analysis can help evaluate the consistency of outcomes across studies. An appropriate blend of scientific information from toxicology and epidemiology studies is necessary to evaluate potential human risks of exposure to neurotoxic substances.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".