A bibliometric analysis of cerebral palsy from 2003 to 2022
Bibliographic record
Abstract
Purpose This bibliometric study explores cerebral palsy (CP) research from 2003 to 2022 to reveal the topic hotspots and collaborations. Methods We retrieved studies on CP from the Web of Science Core Collection from 2003 to 2022 and then used CiteSpace and Bibliometrix to perform a bibliometric analysis and attain knowledge mapping, including publication outputs, funding, journals, authors, institutions, countries/territories, keywords, collaborative relationships, and topic hotspots. Results In total, 8,223 articles were published from 2003 to 2022. During this period, the number of publications increased continuously. Developmental Medicine and Child Neurology was the most productive and frequently co-cited journal. Boyd was the most productive and influential author, with 143 publications and 4,011 citations. The United States and Vrije Universiteit Amsterdam were the most productive countries and institutions, respectively. Researchers and institutions from the USA, Australia, and Canada constituted the core research forces, with extensive collaborations worldwide. The most common keywords were gait (553), rehabilitation (440), spasticity (325), botulinum toxin (174), therapy (148), upper extremity (141), quality of life (140), disability (115), pain (98), electromyography (97), kinematics (90), balance (88), participation (85), and walking (79). Conclusion This study provides a systematic and comprehensive analysis of the CP-related literature. It reveals that Developmental Medicine and Child Neurology is the most active journal in this field. The USA, Vrije Universiteit Amsterdam, and Boyd are the top countries, institutions, and authors, respectively. Emerging treatment methods, complication management, and functional recovery comprise the future research directions and potential topic hotspots for CP.
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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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.205 | 0.285 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".