Vascular Surgery Research in the Coronavirus Disease 2019 Pandemic: A Sex-Based Bibliometric Analysis
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has disrupted vascular surgery services globally and its impact on researchers has illustrated disproportionate barriers for female researchers. We assessed the pandemic's consequences on bibliometric trends in vascular surgery and vascular medicine throughout the pandemic. METHODS: A scoping review was performed using the PubMed/MEDLINE, Scopus, and EMBASE databases from January to December 2020 to identify articles related to COVID-19 and vascular surgery or vascular medicine. Articles only describing cardiac or neurovascular care were excluded. The scoping review was performed according to the PRISMA-ScR guidelines. Bibliometric data were extracted and analyzed. RESULTS: Four hundred and fourteen articles were identified, including 125 (30.2%) original articles, 42 (10.1%) review papers, 105 (25.4%) case reports, 27 (6.5%) editorials and commentaries, 94 (22.7%) letters and correspondences, and 21 (5.1%) conference abstracts. The 5 most common countries of study or discussion were all high-income countries. English was the predominant (n = 393, 94.9%) language. Funding was reported for 5.1% (n = 21) of articles. In the first 6 months, 17.6% (n = 30) of first authors and 10.6% (n = 18) of last authors were female, while the last 6 months saw an increase in representation to 30.6% (n = 74) and 15.6% (n = 38) for first and last author, respectively. CONCLUSION: The pandemic caused a rapid surge in vascular publications related to COVID-19. Female authors remain underrepresented in vascular research and the share in female authorship has dropped early in the pandemic, but rose after the end of the first wave. High-income countries remain overrepresented in research productivity, alluding to important disparities in COVID-19-related literature.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.035 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.207 | 0.228 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".