Scientific and Regulatory Policy Committee Points to Consider Review
Bibliographic record
Abstract
Standard components of nonclinical toxicity testing for novel pharmaceuticals include clinical and anatomic pathology, as well as separate evaluation of effects on reproduction and development to inform clinical development and labeling. General study designs in regulatory guidances do not specifically mandate use of pathology or reproductive end points across all study types; thus, inclusion and use of these end points are variable. The Scientific and Regulatory Policy Committee of the Society of Toxicologic Pathology (STP) formed a Working Group to assess the current guidelines and practices on the use of reproductive, anatomic pathology, and clinical pathology end points in general, reproductive, and developmental toxicology studies. The Working Group constructed a survey sent to pathologists and reproductive toxicologists, and responses from participating organizations were collected through the STP for evaluation by the Working Group. The regulatory context, relevant survey results, and collective experience of the Working Group are discussed and provide the basis of each assessment by study type. Overall, the current practice of including specific end points on a case-by-case basis is considered appropriate. Points to consider are summarized for inclusion of reproductive end points in general toxicity studies and for the informed use of pathology end points in reproductive and developmental toxicity studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.169 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.026 | 0.012 |
| Open science | 0.016 | 0.007 |
| Research integrity | 0.104 | 0.031 |
| Insufficient payload (model declined to judge) | 0.038 | 0.057 |
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".