The effect direction plot revisited: Application of the 2019 Cochrane Handbook guidance on alternative synthesis methods
Bibliographic record
Abstract
Effect direction (evidence to indicate improvement, deterioration, or no change in an outcome) can be used as a standardized metric which enables the synthesis of diverse effect measures in systematic reviews. The effect direction (ED) plot was developed to support the synthesis and visualization of effect direction data. Methods for the ED plot require updating in light of new Cochrane guidance on alternative synthesis methods. To update the ED plot, statistical significance was removed from the algorithm for within-study synthesis and use of a sign test was considered to examine whether patterns of ED across studies could be due to chance alone. The revised methods were applied to an existing Cochrane review of the health impacts of housing improvements. The revised ED plot provides a method of data visualization in synthesis without meta-analysis that incorporates information about study characteristics and study quality, using ED as a common metric, without relying on statistical significance to combine outcomes of single studies. The results of sign tests, when appropriate, suggest caution in over-interpreting apparent patterns in effect direction, especially when the number of included studies is small. The revised ED plot meets the need for alternative methods of synthesis and data visualization when meta-analysis is not possible, enabling a transparent link between the data and conclusions of a systematic review. ED plots may be particularly useful in reviews that incorporate nonrandomized studies, complex systems approaches, and diverse sources of evidence, due to the variety of study designs and outcomes in such reviews.
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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 | 0.433 | 0.782 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".