Considerations for the Identification and Conveyance of Clinical Pathology Findings in Preclinical Toxicity Studies: Results From the 9th ESTP International Expert Workshop
Bibliographic record
Abstract
The European Society of Toxicologic Pathology (ESTP) organized a panel of 24 international experts from many fields of toxicologic clinical pathology (e.g., industry, academia, and regulatory) that came together in 2021 to align the use of terminology to convey the importance of clinical pathology findings in preclinical toxicity studies. An additional goal consisted of how to identify important findings in standard and nonstandard clinical pathology associated endpoints. This manuscript summarizes the information and opinions discussed and shared at the ninth ESTP International Expert Workshop, April 5 to 6, 2022. In addition to terminology usage, the workshop considered topics related to the identification and conveyance of the importance of test item-related findings. These topics included sources of variability, comparators, statistics, reporting, correlations to other study data, nonstandard biomarkers, indirect/secondary findings, and an overall weight-of-evidence approach.
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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 | 0.484 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".