Enhanced Recovery After Craniotomy: Global Practices, Challenges, and Perspectives
Bibliographic record
Abstract
The global demand for hospital care, driven by population growth and medical advances, emphasizes the importance of optimized resource management. Enhanced Recovery After Surgery (ERAS) protocols aim to expedite patient recovery and reduce health care costs without compromising patient safety or satisfaction. Its principles have been adopted in various surgical specialties but have not fully encompassed all areas of neurosurgery, including craniotomy. ERAS for craniotomy has been shown to reduce the length of hospital stay and costs without increasing complications. ERAS protocols may also reduce postoperative nausea and vomiting and perioperative opioid requirements, highlighting their potential to enhance patient outcomes and health care efficiency. Despite these benefits, guidelines, and strategies for ERAS in craniotomy remain limited. This narrative review explores the current global landscape of ERAS for craniotomy, assessing existing literature and highlighting knowledge gaps. Experts from 26 countries with diverse cultural and socioeconomic backgrounds contributed to this review, offering insights about current ERAS protocol applications, implementation challenges, and future perspectives, and providing a comprehensive global overview of ERAS for craniotomy. Representatives from all 6 World Health Organization geographical world areas reported that barriers to the implementation of ERAS for craniotomy include the absence of standardized protocols, provider resistance to change, resource constraints, insufficient education, and research scarcity. This review emphasizes the necessity of tailored ERAS protocols for low and middle-income countries, addressing differences in available resources. Acknowledging limitations in subjectivity and article selection, this review provides a comprehensive overview of ERAS for craniotomy from a global perspective and underscores the need for adaptable ERAS protocols tailored to specific health care systems and countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".