Scientometric analysis of telerehabilitation services for cerebral palsy patients: A comprehensive review and research trends mapping
Bibliographic record
Abstract
BACKGROUND: Telerehabilitation is used for delivering rehabilitation interventions, assessments, and education remotely by communication technology. The current study aimed to conduct a bibliometric analysis to discover research trends and knowledge mapping of telerehabilitation in patients with cerebral palsy. MATERIALS AND METHODS: This descriptive-analytical study was conducted using a bibliometric analysis. The Web of Science was searched and 1,222 records were retrieved from the beginning to January 2024. The usual bibliometric rules were applied using the VOSviewer software for data processing. Also, this study used the LDA algorithm for topic modeling with Python. RESULTS: The findings indicate that the trend of research in the telerehabilitation of patients with cerebral palsy is continuously increasing. Universities and institutions with high publication volumes are mainly institutions from the United States and Canada. The results demonstrates that the co-authorship network of researchers has an average density. Co-occurrence analysis primarily focuses on subjects such as rehabilitation, children, robots, virtual reality for walking and mobility. This research represents the latest evidence-based insights aimed at not only organizing existing knowledge but also assisting researchers and policymakers in achieving a better understanding of telerehabilitation interventions for cerebral palsy patients. CONCLUSION: According to the findings of this research, both the speed of evolution and diversity in telerehabilitation interventions and the amount of access and utilization of these interventions by cerebral palsy patients is high.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.098 | 0.107 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".