Exploring the top-cited literature in telerehabilitation for joint replacement using the descriptive, diagnostic, predictive, and prescriptive analytics model: A thematic and bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Telerehabilitation offers a novel approach supplementing or replacing traditional physical rehabilitation. While research on telerehabilitation for joint replacement (TJR) has expanded, no study has investigated the top 100 cited articles (T100TJR) using the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model. This study aims to examine the features of T100TJR in TJR through the DDPP approaches. METHODS: A comprehensive search of the Web of Science Core Collection was conducted to locate all pertinent English-language documents from the database's inception until August 2, 2023. The T100TJR articles were then identified based on citation counts. The DDPP analytics model, along with 7 visualization techniques, was used to analyze metadata elements such as countries, institutions, journals, authors, references, and keywords. An impact timeline view was employed to highlight 2 particularly noteworthy articles. RESULTS: We analyzed 712 articles and observed a consistent upward trend in publications, culminating in a noticeable peak in 2022. The United States stood out as the primary contributor. A detailed examination of the top 100 articles (T100TJR) revealed the following leading contributors since 2010: the United States (by country), University of Sherbrooke, Canada (by institutions), 2017 (by publication year), and Dr Hawker from Canada (by authors). We delineated 4 major themes within these articles. The theme "replacement" dominated, featuring in 89% of them. There was a strong correlation between the citations an article garnered and its keyword prominence (F = 3030.37; P < .0001). Additionally, 2 particularly high-impact articles were underscored for recommendation. CONCLUSIONS: Telerehabilitation for TJR has seen rising interest, with the U.S. leading contributions. The study highlighted dominant themes, especially "replacement," in top-cited articles. The significant correlation between article citations and keyword importance indicates the criticality of keyword selection. The research underscores the importance of 2 pivotal articles, recommending them for deeper insights.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.157 | 0.155 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".