Structural Approach to Bias in Meta‐analyses
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
Methods to calculate bias-adjusted estimates for meta-analyses are becoming more popular. The objective of this paper is to use the structural approach to bias and causal diagrams to show that (i) the current use of the bias-adjusted estimating tools may sometimes introduce bias rather than reduce it and (ii) the Cochrane collaboration risk of bias tool, which was designed for randomized studies, is also applicable to non-randomized studies with only minimal changes. Causal diagrams are used to illustrate each of the items in the current risk of bias tool and how they apply to both randomized and non-randomized studies. With the exception of confounding by indication, the structure of all potential biases present in non-randomized studies may also be present in randomized studies. In addition, causal diagrams demonstrate important limitations to the methods currently being developed to provide bias-adjusted estimates of individual studies in meta-analyses. Finally, causal diagrams can be helpful in deciding when it is appropriate to combine studies in a meta-analysis of non-randomized studies even though the studies may use different adjustment sets. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.638 | 0.456 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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 it