The Most Cited Mesh Terms and Authors who Published Papers in Pubmed Central on the Topic of Medicine and Health Using Bibliometric Analyses
Bibliographic record
Abstract
Aims: We visualized the current state of research on publication outputs and citations in the field of medicine and health to uncover topic burst and citations among medical subject headings (MeSH) clusters. Study Design: A bibliometric analysis. Place and duration of Study: Using Pubmed indexed articles to inspect the characteristics of topics on medicine and health since 1969. Methodology: Selecting 156 abstracts, author names, countries, and MeSH terms on January 10, 2019, from Pubmed Central (PMC) based on the terms of medicine and health in the title since 1969, we applied the x-index and impact factor to evaluate author individual research achievements and compute MeSH bibliometric performances. The bootstrapping method was used to estimate the median and its 95% confidence intervals and make differences in metrics among MeSH clusters. The dominant nations were selected using the x-index to display on a dashboard. We programmed Microsoft Excel VBA routines to extract data. Google Maps and Pajek software were used for displaying graphical representations. Results: We found that (1)the dominant countries/areas are the Unlited States, Taiwan, and Australia; (2) the author Grajales, Francisco Jose 3rd form Canada has the most cited metrics such as author IF=39.46 and x-index=6.28; (3)the MeSH terms of organization & administration, standards, and prevention & control gain the top three degree centralities among MeSH clusters; (4) No any differences in metrics were found among MeSH clusters; (5) the article(PMID= 24518354) with three MeSH term of delivery of health care, social media, and software and published in 2014 was cited most at least 62 times. Conclusion: Social network analysis provides wide and deep insight into the relationships among MeSH terms. The MeSH weighted scheme and x-index were recommended to academics for computing MeSH citations in the future.
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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 | 0.009 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.148 | 0.145 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".