A 25-year retrospective of Canadian plastic surgery research and its influence: a bibliometric study
Bibliographic record
Abstract
BACKGROUND: Bibliometric analysis is a research tool for evaluating and analyzing scholarly output and impact within a specific domain. This study aimed to assess the quantity and quality of plastic surgery research conducted by Canadian-affiliated authors from 1999 to 2023. METHODS: We conducted a comprehensive bibliometric analysis using the Web of Science Core Collection to retrieve data from 60 leading plastic surgery journals, focusing on original articles and reviews published between 1999 and 2023. The InCites Benchmarking & Analytics platform evaluated the publications' quantity and quality. Quality assessment employed 2 key metrics:: category-normalized citation impact (CNCI) and the percentage of publications in the top quartile of journals (%Q1) based on impact factors. We used VOSviewer to map collaborative relationships among universities over various periods. RESULTS: Canada ranked as the 11th leading contributor globally, with 4446 publications. Nationally, the University of Toronto accounted for more than 30% of Canadian contributions. In terms of quality, Canada led with a CNCI of 1.09 and 21% of publications in the %Q1. Within Canada, McMaster University had the highest CNCI at 1.33, while Dalhousie University ranked highest in %Q1 at 32.3%. Our VOSviewer map of institutional collaborations revealed increased cooperation between Canadian universities and international institutions over the last 25 years. CONCLUSION: Over the last 25 years, the trajectory of Canadian plastic surgery literature has been characterized by continuous expansion while maintaining high quality. Efforts should be made to continue to increase the quality and quantity of Canadian research while sustaining international collaborations.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.093 | 0.195 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".