Visualization toolkits for enriching meta-analyses through evidence maps, bibliometrics, and alternative impact metrics
Bibliographic record
Abstract
Data visualization is crucial for effectively communicating knowledge in meta-analysis. However, existing visualization methods in meta-analysis have predominantly focused on quantitative aspects, such as forest plots and funnel plots, thereby neglecting qualitative information that is equally important for end-users in science, policy, and practice. We introduce a framework consisting of a series of visualization toolkits designed to enrich meta-analyses by borrowing approaches from other research synthesis methods, including systematic evidence mapping (scoping reviews), bibliometrics (bibliometric analysis), and alternative impact metric analysis. These "enrichment" toolkits aim to facilitate the synthesis of both quantitative and qualitative evidence, along with the assessment of the academic and nonacademic influences of the meta-analytic evidence base. While the meta-analysis yields quantitative insights, the enrichment analyses, and visualizations provide user-friendly summaries of qualitative information on the evidence base. For example, a systematic evidence map can visualize study characteristics, unraveling knowledge gaps and methodological differences. Bibliometric analysis offers a visual assessment of the nonindependent evidence, such as hyper-dominant authors and countries, and funding sources, potentially informing the risk of bias. Alternative impact metric analysis employs alternative metrics to gauge societal influence and research translation (e.g., policy and patent citations) of studies in the meta-analysis. We provide a dedicated webpage showcasing sample visualizations and providing step-by-step implementation in open-source software R (https://yefeng0920.github.io/MA_Map_Bib/). Additionally, we offer a guide on leveraging three commercially free large language models (LLMs) to help adapt the sample script, enabling users with less R coding experience to visualize their own meta-analytic evidence base.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.042 | 0.157 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.017 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".