The GRADE approach and Bradford Hill's criteria for causation
Bibliographic record
Abstract
This article describes how the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to grading the quality of evidence and strength of recommendations considers the Bradford Hill criteria for causation and how GRADE may relate to questions in public health. A primary concern in public health is that evidence from non-randomised studies may provide a more adequate or best available measure of a public health strategy's impact, but that such evidence might be graded as lower quality in the GRADE framework. GRADE, however, presents a framework that describes both criteria for assessing the quality of research evidence and the strength of recommendations that includes considerations arising from the Bradford Hill criteria. GRADE places emphasis on recommendations and in assessing quality of evidence; GRADE notes that randomisation is only one of many relevant factors. This article describes how causation may relate to developing recommendations and how the Bradford Hill criteria are considered in GRADE, using examples from the public health literature with a focus on immunisation.
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 | 0.249 | 0.589 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.051 | 0.025 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.013 | 0.012 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".