Workshop Report: Evaluation of Epidemiological Data Consistency forApplication in Regulatory Risk Assessment
Bibliographic record
Abstract
Epidemiological study results have a key role in the assessment of health risks associated with exposures to chemicals and pollutants, and often serve as the basis for the development of regulatory limits for environmental and occupational health. A key uncertainty in the application of epidemiological study results in risk assessments stems from variability in defining and operationalizing the concept of consistency of findings across studies, with assessments of consistency often a controversial component of risk assessments. Although assessment of consistency of findings across a diverse collection of epidemiological studies is central to evaluating that body of evidence for supporting causal inferences, the variability in definition and formal evaluation methods strongly suggest the need for constructive approaches to consistently and transparently evaluate data consistency. In response to the need to improve approaches to assessing consistency in epidemiological study results, the Johns Hopkins Risk Sciences and Public Policy Institute organized a workshop held in Baltimore, Maryland in September 2010 to identify and discuss key methodological issues, and to develop recommendations for qualitative and quantitative approaches to addressing those issues. A multi-disciplinary approach was utilized for the workshop, involving invited experts from a variety of fields, and the invited participants were drawn from academia, industry, government, and the public interest sectors. This report provides a summary of selected epidemiology methodological issues discussed by the workshop participants and provides the workshop’s key findings and recommendations for future approaches to addressing this issue.
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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 | 0.438 | 0.430 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".