A bibliometric analysis of Prader-Willi syndrome from 2002 to 2022
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.057 |
| 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) | 0.003 | 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 it