A new threshold dose‐response model including random effects for data from developmental toxicity studies
Bibliographic record
Abstract
Usually, in teratological dose finding studies, there are not only threshold effects but also extra variations that cannot be accounted for by the beta-binomial model alone. The beta-binomial model assumes correlation between fetuses in the same litter. The general random effect threshold (RE) model allows the additional variability that arises due to correlation and between litter variability to be modeled, in combination with threshold in the model. The goal of this research was to investigate a threshold dose-response model with random effects (RE) to model the variability that exists between litters of animals in studies of toxic agents. Data from a developmental toxicity study of a toxic agent were analysed, using the proposed RE threshold dose-response model, which is an extension of logit in form. Also, an approximate likelihood function was used to derive parameter estimates from this model, and tests were performed to determine the significance of the model parameters, in particular, the RE parameter. A simulation study was conducted to assess the performance of the RE threshold model in estimating the model parameters.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".