Adverse Outcome Pathway (AOP) Coaching Program—how it functions and contributes to a more harmonized approach to AOP development and construction of AOP networks with regulatory utility
Bibliographic record
Abstract
The adverse outcome pathway (AOP) framework contributes to understanding how specific and measurable biological perturbations cause adverse effects on human and environmental health. Recognizing the value of AOPs to support regulatory decisions around the world, the Organisation for Economic Co-operation and Development (OECD) launched the AOP Programme in 2012, which sought to promote and guide the development of AOPs to ensure their suitability for the downstream applications in the context of regulatory safety assessment. The OECD published the initial guidance on AOP development and assessment in 2013, which has been expanded as practices have evolved and matured. Adverse outcome pathway development requires adherence to specific principles and considerations for identifying and describing key events (KEs) and representing and assessing the weight of evidence for the key event relationships. Ultimately, the structured and consistent application of the principles helps build confidence in the applicability of the knowledge represented in the AOP for decision-making in the regulatory context. To assist new AOP developers, in 2019, the OECD introduced a coaching program. This program primarily aims to pair novices with experienced AOP developers (i.e., coaches). International partnerships in the coaching program contribute to harmonizing and promoting AOP development according to OECD guidance. Coaches have also helped to identify and initiate "gardening" efforts that remove redundant/synonymous KEs in the AOP-Wiki, allowing for improved AOP network creation, promoting the reuse of extensively reviewed KEs, and ensuring the development of high-quality AOPs. The AOP Coaching Program represents the latest international activity to ensure that AOPs are developed in a consistent manner that is designed to enhance their use for supporting public health decisions around the world.
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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 | 0.047 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".