International publication trends originating from anaesthetic departments from 2001 to 2015
Bibliographic record
Abstract
The aim of this study was to analyse publication trends from the anaesthetic literature of the G-20 countries. We performed a literature search in Medline to identify articles related to anaesthetic departments published between 2001 and 2015, by specific G-20 countries according to the affiliation field of the authors, and to three time periods 2001-2005, 2006-2010 and 2011-2015. The number of articles, number of original articles (vs. reviews, editorials or correspondence), articles per million inhabitants, and citations per article were analysed. In total, 96,920 articles were published between 2001 and 2015 in 74 anaesthetic and in 4117 non-anaesthetic journals, with an increase of +104% absolute (i.e. from 23,028 in 2001-05 to 46,887 articles ìn 2010-15) and +85% as articles per million inhabitants. Similarly, the number of original articles increased by 21%, but the anaesthetic specialty's share of original articles (as a proportion of total articles in biomedicine) decreased from 31% in 2001-2005 to 19% in 2011-2015 (-38%). The USA published most articles (2011-15 16,016; 31% of total), second came the EU as a whole and third Japan (from 2001 to 2005) or Germany (2006-2010) until 2011-2015 when China took over the third rank. In 2011-2015, Canada published most articles per million inhabitants (68.7 articles/million inhabitants). China and India exhibited the most publication growth 11- and 9-fold, respectively, and are now among the top five countries for the number of published articles.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.063 | 0.083 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".