History and publication trends in the diffusion and early uptake of indirect comparison meta-analytic methods to study drugs: animated coauthorship networks over time
Bibliographic record
Abstract
OBJECTIVE: To characterise the early diffusion of indirect comparison meta-analytic methods to study drugs. DESIGN: Systematic literature synthesis. DATA SOURCES: Cochrane Database of Systematic Reviews, EMBASE, MEDLINE, Scopus and Web of Science. STUDY SELECTION: English language papers that used indirect comparison meta-analytic methods to study the efficacy or safety of three or more interventions, where at least one was a drug. DATA EXTRACTION: The number of publications and authors was plotted by year and type: methodological contribution, review or empirical application. Author and methodological details were summarised for empirical applications, and animated coauthorship networks were created to visualise contributors by country and affiliation type (academia, industry, government or other) over time. RESULTS: We identified 477 papers (74 methodological contributions, 42 reviews and 361 empirical applications) by 1689 distinct authors from 1997 to 2013. Prior to 2002, only three applications were published, with contributions from the USA (n=2) and Canada (n=1). The number of applications gradually increased annually with rapid uptake between 2011 and 2013 (n=254, 71%). Early diffusion occurred primarily in Europe with the first application credited to the UK in 2003. Application spread to other European countries in 2005, and may have been supported by regulatory requirements for drug approval. By the end of 2013, contributions included 49% credited to Europe (22% UK, 27% other), 37% credited to North America (11% Canada, 26% USA) and 14% from other regions. CONCLUSION: Indirect comparison meta-analytic methods are an important innovation for health research. Although Canada and the USA were the first to apply these methods, Europe led their diffusion. The increase in uptake of these methods may have been facilitated by acceptance by regulatory agencies, which are calling for more comparative drug effect data to assist in drug accessibility and reimbursement decisions.
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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 | 0.206 | 0.623 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.031 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".