Forest plots in reports of systematic reviews: a cross-sectional study reviewing current practice
Bibliographic record
Abstract
BACKGROUND: Forest plots are graphical displays of findings of systematic reviews and meta-analyses. Little is known about the style and content of these plots and whether published plots maximize the graphic's potential for information exchange. METHODS: We examine the number, style and content of forest plots presented in a previously studied cross-sectional sample of 300 systematic reviews. We studied all forest plots in non-Cochrane reviews and a sample of forest plots in Cochrane reviews. RESULTS: The database contained 129 Cochrane reviews and 171 non-Cochrane reviews. All the Cochrane reviews had forest plots (2197 in total), and a random sample of 500 of these plots were included. In total, 28 of the non-Cochrane reviews had forest plots (139 in total), all of which were included. Plots in Cochrane reviews were standardized but often contained little data (80% had three or fewer studies; 10% had no studies) and always presented studies in alphabetical order. Non-Cochrane plots depicted a larger number of studies (60% had four or more studies) and 59% ordered studies by a potentially meaningful characteristic, but important information was often missing. Of the 28 reviews that had a forest plots with at least 10 studies, 3 (11%) had funnel plots. CONCLUSIONS: Forest plots in Cochrane reviews were highly standardized but some of the standards do not optimize information exchange, and many of the plots had too little data to be useful. Forest plots in non-Cochrane reviews often omitted key elements but had more data and were often more thoughtfully constructed.
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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 | 0.347 | 0.796 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.043 | 0.054 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".