The impact of Canadian-produced research on the global orthopedic literature: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Little is known about the quality and impact of Canadian-produced research relative to that of other developed nations. The purpose of this study was to determine the contribution of Canadian authors to the orthopedic literature globally and nationally as well as Canada's research productivity in orthopedics. We hypothesized that Canada ranks among the most impactful countries in terms of orthopedic research productivity. METHODS: We performed a bibliometric analysis to identify articles published between 2001 and 2020 in the category of orthopedics. We identified Canada's global rank in terms of overall productivity and assessed the contributions of individual Canadian authors. We also examined the quality of publications as determined by category normalized citation impact (CNCI) and publication in the top quartile of journals (%Q1) in terms of impact factor. In addition, we calculated the percentage of Canadian publications that were in orthopedics. RESULTS: We identified 10 821 orthopedic publications from 2001 to 2020. Canada placed sixth globally in terms of productivity in orthopedic research. The annual productivity of Canadian orthopedic researchers increased over the study period by a factor of 3.2. In terms of research quality, with a %Q1 of 36.5% and a CNCI of 1.22, Canada outperformed Asian countries and the United States; the latter country had a %Q1 of 35.3% and a CNCI of 1.14 over the study period. CONCLUSION: The body of Canadian orthopedic literature has grown consistently over the past 20 years. Despite the overall leadership of the United States and other developed nations such as China and Japan, Canada ranks among the most influential countries in terms of the quality and quantity of orthopedic research.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.122 | 0.223 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".