Visualization studies on evidence‐based medicine domain knowledge (series 2): structural diagrams of author networks
Bibliographic record
Abstract
OBJECTIVE: To investigate the output of evidence-based medicine (EBM) researchers in China and elsewhere by examining the EBM domains they work within and the networks that exist among them; using visualization methods to analyze these relationships. This maps the current situation and helps with the identification of areas for future growth. METHODS: We used co-citation matrixes with Pathfinder networks and hierarchical clustering algorithms, and constructed a co-author matrix which were analyzed with a whole network approach. The analyzed matrixes were visualized with the UCINET program. RESULTS: Much of the development of EBM has been centered around three authors, David Sackett, Gordon Guyatt and L Manchikanti, within three different clusters. The main authors of EBM articles in China were divided into nine academic domains. The relations among core authors of articles indexed by the Science Citation Index (SCI) was loose. There was a stronger co-authorship network among core authors in the Chinese literature, with three groups and 21 cliques. Nine distinct academic communities appeared to have formed around Li Youping, Liu Ming and Zhang Mingming. CONCLUSION: The EBM literature contains several key clusters, with universities in high-income countries being the source of the majority of articles. Outside China, McMaster University in Canada, the original home of EBM, is the dominant producer of EBM publications. In China, Sichuan University is the main source of EBM publications. The EBM cooperation network in China is comprised of three major groups, the largest and most productive in this sample is led by Li Youping with Liu Ming, Zhang Mingming, Li Jing, Wang Li, Wu Taixiang, and Liu Guanjian as central members.
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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 | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".