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.
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 | 0.372 | 0.608 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.026 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".