Toxicologic Pathology Forum*: Opinion on Qualitative Severity Descriptors to Express Magnitude of Changes in Clinical Pathology Endpoints in Nonclinical Toxicity Studies
Bibliographic record
Abstract
Clinical pathology endpoints are routinely assessed in nonclinical toxicity studies and the magnitude of test article-related changes is frequently expressed using quantitative and/or qualitative severity descriptors. Quantitative descriptors (ie, percent or fold change) are easily calculated to express numerical magnitude of a change but may not adequately convey biological relevance. A specific quantitative magnitude may be associated with vastly different levels of pathophysiologic relevance depending on several factors, including the nature of the endpoint, the animal species/strain, and the magnitude and direction of change. Qualitative descriptors (eg, minimal and mild) offer a succinct way to provide additional context to the pathophysiologic relevance but are more challenging to ascribe to a change. The assignment of qualitative descriptors often requires a subjective, comprehensive, and multifaceted approach using various factors in addition to numerical calculation. Because of the subjectivity involved, the qualitative severity descriptor assigned to a specific change may differ among clinical pathology endpoints, species/strain, contributing scientists, and studies/programs. Quantitative and qualitative severity descriptors may provide complementary information and may be used individually or in combination. This opinion piece primarily explains the process and discusses caveats and various factors taken into consideration by clinical pathologists while ascribing qualitative severity descriptors.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".