Bibliometric Analysis of the 100 Most Cited Articles on Dysphagia Rehabilitation
Bibliographic record
Abstract
Objective: This study aims to conduct a bibliometric analysis of the 100 most-cited articles in dysphagia rehabilitation. Methods: A comprehensive search was conducted in the Web of Science database for articles published between January 1975, and July 2024, using the keyword "dysphagia rehabilitation." The 100 most-cited articles were selected for bibliometric analysis. Key data extracted from these articles included the title, publication year, author names, total citation count, citation index, journal of publication, impact factor, and type of article. The citation index was calculated by dividing the total number of citations by the number of years since publication to assess the impact and relevance of each article over time. Results: The T100 articles received a total of 22.674 citations. Overall, 61 journals published the T100 articles, with the Archives of Physical Medicine and Rehabilitation (n=12) being the journal that published the most. The United States, followed by England, Canada and Japan had the highest number of articles. Clinical research was the most common type of article among the T100. A strong relationship was found between the citation index and the number of citations (p ≤ 0.05). Conclusion: Due to its growing need, dysphagia rehabilitation is becoming an increasingly popular research area. These findings can help researchers understand the quality and trends in dysphagia rehabilitation research and guide future studies.
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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 | 0.014 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.206 | 0.184 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".