Global Hotspots and Visualization of Enhanced Recovery After Surgery (ERAS) Protocols: A Bibliometric Analysis
Bibliographic record
Abstract
Objective Enhanced Recovery After Surgery (ERAS) protocols are multidisciplinary, evidence-based pathways that minimize complications, shorten hospital stays, and improve outcomes. This study conducted a bibliometric analysis of global ERAS research to identify trends in publication output, geographic distribution, and institutional influence. Methods A bibliometric search was performed to assess the publication landscape of enhanced recovery after surgery (ERAS) by searching the Web of Science core collection between 2002-–2025. VOSviewer software was used to map keyword co-occurrence. Trends in publication volume, country of origin, and institutional contributions were assessed. Results The search yielded a total of 18,681 documents. The most prolific countries overall were the United States, China, Italy, Canada and India. ERAS publications are most prominent in colorectal, bariatric, and thoracic surgery. Publication output increased steadily over the last decade, underscoring the integration of ERAS principles into clinical practice. Keyword analysis revealed major themes in perioperative care, complications and specific surgical fields. Conclusion ERAS protocols have achieved growing global recognition, supported by expanding research output and leadership from high-income countries and major academic centers. Although publications in developing regions remain limited by resource constraints, ERAS principles are disseminating internationally and establishing themselves as a cost-effective, patient-centered standard in perioperative medicine.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.146 | 0.179 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".