A matrix for bridging the epidemiology and risk assessment gap
Bibliographic record
Abstract
Environmental epidemiologic research provides invaluable information for understanding the relationship between environmental exposures and health outcomes. Chemical risk assessment, a foundation of public health decision-making, benefits from having information from various disciplines including epidemiology. While epidemiology and risk assessment have common goals of understanding and reducing human health impacts associated with exposure to environmental chemicals, each discipline utilizes different terminologies and skill sets. This contributes to the challenges faced when seeking to use human data in risk assessment. For over twenty years, scientists have recognized that dialogue between risk assessors and epidemiologists is crucial, although to date no specific path forward has been developed for this purpose. This need for communication motivated the organization of a workshop to explore the question "What do risk assessors need in order to be able to improve the value of epidemiologic research for use in decision-making?" This paper describes the outcome of the workshop, specifically a Matrix designed as a communication tool. The Matrix includes a description of key elements that when included in epidemiology design and/or reporting enhance the use of epidemiology results for a risk assessment. The Matrix is not intended to supplant best practices for environmental epidemiology or existing frameworks on integrating multidisciplinary data. Rather, the goal of the Matrix is to improve understanding and communication between the disciplines. Bridging the gap between epidemiology and risk assessment will enrich both disciplines and enhance public health decision-making.
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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 | 0.075 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 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".