Regulatory Perspectives on Juvenile Animal Toxicologic Pathology
Bibliographic record
Abstract
The Society of Toxicologic Pathology's Annual Virtual Symposium (2021) included a session on "Regulatory Perspectives on Juvenile Animal Toxicologic Pathology." The following narrative summarizes the key concepts from the four talks included in this symposium session chaired by Drs Deepa Rao and Alan Hoberman. These encompass an overview of various global regulations impacting the conduct of juvenile animal studies in pharmaceutical drug development and chemical toxicity assessments in a talk by Dr Alan Hoberman. Given the numerous regulatory guidances and legal statutes that have covered the conduct of juvenile animal studies and the recent harmonization of these guidances for pharmaceuticals, Dr Paul Brown provided an update on the harmonization of these guidances for pharmaceuticals, in the recently finalized version of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use S11 guidance document, "Nonclinical Safety Testing in Support of Development of Pediatric Medicines." The first two talks on regulations were followed by two talks focused on an evaluation of the postnatal development of two major organ systems relevant in juvenile animals. Dr Aurore Varela covered study design and endpoints impacting the skeletal system (bone), while Dr Brad Bolon presented a talk on the study design and conduct of neuropathology evaluations for the developing nervous system.
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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 | 0.069 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".