Improving Concordance in Environmental Epidemiology: A Three-Part Proposal
Bibliographic record
Abstract
In observational research, evidence is usually derived from multiple studies, and any single result is rarely considered sufficient for public health decision making. Despite more than five decades of research and thousands of studies published, the ability to draw robust conclusions regarding the presence or absence of causal links between specific environmental exposures and human health remains limited. To develop policies that are protective of public health and can withstand scrutiny, agencies need to rely on investigations of satisfactory quality that follow sufficiently concordant protocols in terms of exposure assessment, outcome ascertainment, data analysis, and reporting of results. Absent such concordance, the ability of environmental epidemiology studies to inform decision making is greatly diminished. Systems and tools are proposed here to improve concordance among environmental epidemiology studies. Specifically, working systems in place in other fields of research are critically examined and used as guidelines to develop analogous policies and procedures for environmental epidemiology. A three-part path forward toward more concordant, transparent, and readily accessible environmental epidemiology evidence that parallels ongoing efforts in medical research is proposed. The three parts address methods for improving quality and accessibility of systematic reviews, access to information on ongoing and completed studies, and principles for reporting. The goals are to increase the value of epidemiological research in public health decision making and to stimulate discussions around solutions proposed herein.
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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 | 0.741 | 0.729 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.038 | 0.058 |
| Open science | 0.015 | 0.042 |
| Research integrity | 0.062 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".