Distribution of Country of Origin in Studies Used in Cochrane Reviews
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Inclusion in systematic reviews is one important component in judging the potential impact of clinical studies upon practice and hence the 'value for money' of spending for clinical research. This study aims to quantify the distribution of countries of origin of clinical studies used in Cochrane Reviews (CRs), and to link these data to the size of a country and to its spending on research. METHODS: Random sample of publications used for CRs published in Issue 1 2008 and of publications used in CRs in the field of complementary and alternative medicine (CAM). Publications without original data were excluded. Likely countries of origin determined based on abstracts/full texts. CIA World Factbook (population data) and OECD database (economic data) were used. RESULTS: 1,000 random entries out of 140,005 references available in all specialities. In 876 (91.4%) of 959 eligible studies, country of origin was determined. The USA was the leading contributor (36.0% of the studies), followed by UK (13.4%), Canada (5.3%), Australia and Sweden (3.7%). In the CAM sample, country of origin was determined in 458 (93.5%) of 497 assessed studies. Again, the USA was the leading contributor (24.9%), with China also emerging as a significant contributor (24.7%) in this field. For both samples, the contribution of smaller countries (especially Scandinavian countries, Greece, and Ireland) became more noteworthy when considered in relation to population size and research spending. CONCLUSIONS: Our results support the leading roles of both the USA and the UK in publishing clinical papers. The emerging role of China can be seen, particularly related to CAM studies. Taking into account size of population and economic power, countries like France, Germany, Italy, and Spain provide small contributions. In contrast, smaller countries like Australia, Denmark, Finland, Ireland, New Zealand, and Sweden also play major roles.
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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 | 0.356 | 0.797 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.097 | 0.121 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".