Globalization of Plastic and Reconstructive Surgery: A Continent, Country, and State-Level Analysis of Publications
Bibliographic record
Abstract
Background: Over the past decade, there has been a worldwide increase in plastic and reconstructive surgery research as well as increased interest in global collaboration. However, little is known about who is contributing to this global expansion or the trends of individual countries. The aim of our study was to analyze the output of Plastic and Reconstructive Surgery (PRS) over a decade to elucidate trends in the plastic surgery field. Methods: The country of origin for all first authors of articles published by PRS from 2010 to 2019 were determined and date extracted using PubMed2XL. The change in frequency of publications over the decade by country, continental contributions, as well as state-level analysis within the United States were analyzed. Results: From 2010 to 2019, there were a total number of 8680 publications with an increase in total articles from 747 to 1049 per year. 54 countries contributed over the decade, with the United States producing the most followed by Italy, China, Canada, and the UK. The top producing states were Texas, New York, California, Massachusetts, and Pennsylvania. Conclusions: The last decade (2010–2019) saw a large international increase in research, not only with the total number of publications, but also in the diversity of originating country. Our work shows a shift away from a US-focused journal to incorporate more work from our international colleagues, as research is conducted in centers across the globe.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.033 | 0.050 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".