Prehabilitation of surgical patients: a bibliometric analysis from 2005 to 2023
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: Good preoperative conditions help patients to counteract surgical injury. Prehabilitation is a multimodal preoperative management strategy, including physical, nutritional, psychological, and other interventions, which can improve the functional reserve of patients and enhance postoperative recovery. The purpose of this study is to show the evolution trend and future directions of research related to the prehabilitation of surgical patients. METHODS: The global literature regarding prehabilitation was identified from The Web of Science Core Collection database. Bibliometric methods of the Bibliometrix package of R (version 4.2.1) and VOSviewer were used to analyze publication trends, cooperative networks, study themes, and co-citation relationships in the field. RESULTS: A total of 638 publications were included and the number of publications increased rapidly since 2016, with an average annual growth rate of 41.0%. "Annals of Surgery", "British Journal of Surgery" and "British Journal of Anesthesia" were the most cited journals. Experts from the USA, Canada, the UK, and the Netherlands contributed the most in this field, and an initial cooperative network among different countries and clinical teams was formed. Malnutrition, older patients, frailty, and high-risk patients were the hotspots of recent studies. However, among the top 10 cited articles, the clinical effects of prehabilitation were conflicting. CONCLUSION: This bibliometric review summarized the most influential publications as well as the publication trends and clarified the progress and future directions of prehabilitation, which could serve as a guide for developing evidence-based practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.031 | 0.071 |
| 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) | 0.006 | 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