The future of skin aging studies: An analysis of global trends
Bibliographic record
Abstract
This study presents a bibliometric analysis of global trends in skin aging research, identifying key contributors, dominant themes, and research gaps. A bibliometric analysis was conducted on skin aging research published between 1990 and 2024. Using the Web of Science database, 579 studies were identified with keywords such as "skin aging," "aging mechanisms," and "skin physiology." After screening, 567 articles were analyzed. Text mining and data visualization techniques were applied using VOSviewer to enhance accuracy and interpretability. The analysis included 567 articles with 25,312 citations, averaging 45 citations per article, and an H-index of 83. The number of publications and citations has steadily increased since 2001, with 70% of studies originating from the United States, Canada, and the United Kingdom. Physiology was the leading research category (55%), followed by dermatology, cell biology, and sports science. Additionally, 94% of articles were indexed in SCI-Expanded, indicating strong representation in health sciences. Keyword analysis identified interconnected research clusters, with skin physiology, aging, and skin blood flow as dominant themes. Skin aging research is multidisciplinary, incorporating advanced methodologies such as machine learning and high-throughput omics. Despite significant progress, research gaps persist, particularly in understanding the role of systemic inflammation and disparities in global research output. This study underscores the growing interest in skin aging research, highlighting key trends, challenges, and the need for interdisciplinary collaboration and technological advancements to further explore its systemic implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".