Opportunities and Challenges for the Next Phase of Enhanced Recovery After Surgery
Bibliographic record
Abstract
Importance: Enhanced Recovery After Surgery (ERAS) is a global surgical quality improvement initiative now firmly entrenched within the field of perioperative care. Although ERAS is associated with significant clinical outcome improvements and cost savings in numerous surgical specialties, several opportunities and challenges deserve further discussion. Observations: Uptake and implementation of ERAS Society guidelines, together with ERAS-related research, have increased exponentially since the inception of the ERAS movement. Opportunities to further improve patient outcomes include addressing frailty, optimizing nutrition, prehabilitation, correcting preoperative anemia, and improving uptake of ERAS worldwide, including in low- and middle-income countries. Challenges facing enhanced recovery today include implementation, carbohydrate loading, reversal of neuromuscular blockade, and bowel preparation. The COVID-19 pandemic poses both a challenge and an opportunity for ERAS. Conclusions and Relevance: To date, ERAS has achieved significant benefit for patients and health systems; however, improvements are still needed, particularly in the areas of patient optimization and systematic implementation. During this time of global crisis, the ERAS method of delivering care is required to take surgery and anesthesia to the next level and bring improvements in outcomes to both patients and health systems.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".