A decade of global research activity in pressure ulcer science: a survey of publication patterns (2015–2024)
Bibliographic record
Abstract
OBJECTIVE: While the assessment of scientific research output is common, pressure ulcer (PU) research lacks a comprehensive global survey. This study aimed to evaluate the volume and impact of worldwide PU publications from 2015-2024, as well as delineate global scientific production characteristics. METHOD: Original articles and reviews concerning PUs published between 2015-2024 were retrieved from the Web of Science database. Data extracted for each contributing nation included total publication count, publications per capita and per gross domestic product (GDP), total citations and mean number of citations per article. Countries were categorised by income level and geographical region. RESULTS: The survey identified 7461 publications, with a significant upward trajectory in annual output (p<0.001). East Asia, North America and Western Europe were the most productive regions. High-income economies contributed the substantial majority (72.36%) of articles. The US led in absolute publication volume (1816 articles, 24.34%) and total citations (39,870). When normalised, Turkey ranked highest for number of publications per $100 billion USD GDP (24.41), while Australia led on number of publications per 10 million population (140.29). Canada achieved the highest mean number of citations per paper (27.33). A significant positive correlation was observed between numbers of publications and national GDP (r=0.574; p=0.010), but not with population size (r=0.366; p=0.132). CONCLUSION: Global research output on PUs has markedly increased over the past decade, predominantly driven by high-income nations. The US is the leading contributor in absolute terms, whereas countries such as Australia and Turkey exhibit strong relative productivity. National economic status significantly correlated with research contributions in this domain.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".