A practical guide to conducting dose-response meta-analyses in epidemiology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dose-response relationships between continuous risk factors and disease outcomes are necessary for understanding the risks related to different levels of exposure. Dose-response risk curves can lead to more targeted public health messaging, prevention efforts, and policy implementation. Meta-analyses are often used to combine statistical results from different studies and can be used to model dose-response relationships. However, several challenges are encountered when performing dose-response meta-analysis, such as having heterogeneous reference categories, inconsistent measures of risk, and determining the most accurate shape of the curve. In this paper, we propose a three-step process for estimating dose-response relationships via meta-analysis, which involves: 1) harmonizing the measures of risk, 2) homogenizing the reference category, and 3) selecting meta-regression models. We use data obtained from a systematic review on the dose-response relationship between alcohol consumption and the risk of chronic liver disease to provide an example of the proposed process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it