Analysis of Hotspots and Frontiers of Allostatic Load Research: a Visualization Analysis Based on CiteSpace
Bibliographic record
Abstract
Background Allostatic load (AL) is a multi-systematic physiological indicator to measure chronic stress in the body, reflecting the cumulative physiological burden of chronic stress exposure on the body, which is of great significance for prevention and treatment of chronic diseases. In recent years, studies related to AL are gradually increasing, however, the development of domestic AL related studies is slow. Reviewing the literature related to AL and comprehensively understanding the development trends and hotspots in this field will be beneficial to prompt domestic innovative development of AL research in China. Objective To understand the research hotspots and frontiers in the field of AL and provide reference for future research by analyzing the relevant literature on AL published in recent years. Methods Science Citation Index Expanded of Web of Science Core Collection database was searched for literature on the topic of AL from inception to 2022-12-01. Microsoft Excel 2019 and CiteSpace software were applied to perform a visualization analysis of collected articles regarding the publication volume, countries, authors, institutions and keywords. Results A total of 509 articles were included, with a slowly increasing trend in annual publication volume. The United States ranked first in annual publication volume (315 articles) and betweenness centrality (0.65) . The top three authors in terms of publication volume were JUSTER of Canada (23 articles) , SEEMAN of the United States (16 articles) and KARLAMANGLA of the United States (12 articles) . The top three institutions in terms of publication volume were the University of California, Los Angeles (39 articles) , the University of Montreal in Canada (21 articles) and the University of Michigan in the United States (20 articles) , respectively. High-frequency keywords included AL, stress, health, socio-economic status, cumulative biological risk. Blood pressure was the keyword with the strongest citation bursts, the strength was 5.8. Conclusion The research on AL is gradually becoming a new academic hotspot, which mainly focuses on the influencing factors of AL and its relationship with health outcomes, with rare intervention studies. The domestic development in this field still needs to be further explored. Expanding the scope of research participants, formulating AL measurement methods for different populations and forming norms, as well as performing relevant intervention research should be considered in future studies to promote human health.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.083 | 0.067 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".