Table_1_Research hotspots and new trends in the impact of resistance training on aging, bibliometric and visual analysis based on CiteSpace and VOSviewer.pdf
Bibliographic record
Abstract
Purpose<p>Resistance training (RT) can intervene in aging, which can effectively improve trainees’ life. However, unhealthy living habits such as irregular life, obesity and hyperlipidemia, and chronic diseases lead to a significant decline in the energy level of the population, seriously affecting the health of the population. Our research identifies the research hotspots of RT to intervene in aging from the perspective of bibliometrics, predicts research frontiers and development trends, and provides more perspectives for research on aging populations.</p>Methods<p>In this study, we used CiteSpace and VOSviewer visualization software to draw the scientific knowledge map of countries/regions, institutions, authors, co-occurrence keywords, and co-cited references of published articles, and explore the Web of Science core collection database all about the RT intervention aging research status, hotspots, frontiers, and development trends of articles on aging.</p>Results<p>Among the 760 articles that meet the inclusion criteria, the number of articles published and the frequency of citations have increased steadily in the past 5 years. Judging from the countries/regions, institutions, scholars, and journals that published articles, the ones with the largest numbers are the USA, Univ Estadual Londrina, Cyrino ES, and Exp Gerontol. The ones with the highest influence are England, Univ Arkansas Med Sci, Frontera WR, and Biochem Biophys Rep Co. The top five co-occurrence keywords of include exercise, strength, resistance training, skeletal muscle, and muscle strength. The research frontier is physical function.</p>Conclusion<p>In the field of RT intervention aging research, relevant scholars deserve further in-depth research and exploration. The United States, Brazil, Canada, and other economically developed countries/regions, institutions, and authors have greater influence and productivity. These quantitative research results can provide references for relevant scholars’ follow-up research and government departments to formulate and modify health policies or measures.</p>
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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: Dataset About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.135 | 0.215 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".