A bibliometric analysis of Prader-Willi syndrome from 2002 to 2022
Bibliographic record
Abstract
Background: Prader-Willi Syndrome (PWS) is a rare disorder that was initially documented by Prader and Willi in 1956. Despite significant advancements in the understanding of PWS over recent decades, no bibliometric studies have been reported on this field. We aimed to analyze and explore the research trends and hotspots of PWS using a bibliometric analysis to understand the future development of basic and clinical research. Methods: The literature regarding PWS was retrieved from the Web of Science Core Collection Science Citation Index Expanded (SCI-Expanded) database. Data were extracted from the articles or review articles, and analyzed using CiteSpace and VOSviewer software. Results: A total of 1,895 related studies have been published in 64 countries or regions. The United States has published the most articles, followed by the United Kingdom, Italy, Netherlands, and France. University of Florida (The United States), University of Kansas (The United States), University of Alberta (Canada), University of Cambridge (the United Kingdom), and Dutch Growth Research Foundation (Netherlands) were the top five most productive institutions. Butler, Merlin G. and his colleagues have made the most outstanding contributions in the field of PWS research. Keyword co-occurrence analysis showed that genomic imprinting, uniparental disomy, obesity, hyperphagia, hypothalamus, growth hormone treatment, and ghrelin appeared with the higher frequency. Furthermore, oxytocin, magel2, and management were the latest bursts keywords. Conclusion: Our findings indicated that genetic mechanism, diagnose, and emerging therapies will be the hotspots and frontiers in PWS research.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.192 | 0.234 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, 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".