A framework for fit-for-purpose dose response assessment
Bibliographic record
Abstract
The NRC report Science and Decisions: Advancing Risk Assessment made several recommendations to improve chemical risk assessment, with a focus on in-depth chronic dose-response assessments conducted by the U.S. Environmental Protection Agency. The recommendations addressed two broad elements: improving technical analysis and utility for decision making. To advance the discussions in the NRC report, in three multi-stakeholder workshops organized by the Alliance for Risk Assessment, available and evolving risk assessment methodologies were considered through the development and application of case studies. A key product was a framework (http://www.allianceforrisk.org/Workshop/Framework/ProblemFormulation.html) to guide risk assessors and managers to various dose-response assessment methods relevant to a range of decision contexts ranging from priority setting to full assessment, as illustrated by case studies. It is designed to facilitate selection of appropriate methodology for a variety of problem formulations and includes a variety of methods with supporting case studies, for areas flagged specifically by the NRC committee for consideration--e.g., susceptible sub-populations, population variability and background. The framewok contributes to organization and communication about methodologies for incorporating increasingly biologically informed and chemical specific knowledge into dose-response analysis, which is considered critical in evolving fit-for-purpose assessment to address relevant problem formulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".