The Society of Toxicologic Pathology: Advances and Adventures in the First 50 Years
Bibliographic record
Abstract
The Society of Toxicologic Pathology (STP, https://www.toxpath.org/) was founded in North America in 1971 as a nonprofit scientific and educational association to promote the professional practice of pathology as applied to pharmaceutical and environmental safety assessment. In the ensuing 50 years, the STP has become a principal global leader in the field. Society membership has expanded to include toxicologic pathologists and allied scientists (eg, toxicologists, regulatory reviewers) from many nations. In addition to serving membership needs for professional development and networking, major STP outreach activities include production of articles and presentations designed to optimize toxicologic pathology procedures ("best practice" recommendations), communicate core principles of pathology evaluation and interpretation ("points to consider" and "opinion" pieces), and participation in international efforts to harmonize diagnostic nomenclature. The STP has evolved into an essential resource for academic, government, and industrial organizations that employ and educate toxicologic pathologists as well as use toxicologic pathology data across a range of applications from assessing product safety (therapies, foods, etc) to monitoring and maintaining environmental and occupational health. This article recapitulates the important milestones and accomplishments of the STP during its first 50 years.
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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 | 0.033 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".