Optimizing Recovery in Cardiac Surgery: A Narrative Review of Enhanced Recovery After Surgery Protocols and Clinical Outcomes
Why this work is in the frame
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Bibliographic record
Abstract
Enhanced Recovery After Surgery (ERAS) is an evidence-based, holistic perioperative recovery protocol intended to improve patient outcomes and decrease postoperative complication rates. While ERAS protocols were first introduced in 1997, specific guidelines for cardiac surgery were not established until 2019. Although the core principles of ERAS remain constant across surgical disciplines, ERAS guidelines for cardiac surgery have remained relatively understudied, likely due to the unique complexities posed by cardiac procedures. Within this comprehensive narrative review, we aimed to explore the current guidelines and evidence for ERAS in both cardiac and non-cardiac surgeries. In non-cardiac surgeries, ERAS has been shown to improve various outcomes, including ICU length of stay, patient satisfaction, and pain management. ERAS for cardiac surgery has also shown encouraging results, including shorter ICU and hospital stays, reduced postoperative opioid use, and faster recovery times. However, there is limited consensus across studies, particularly regarding its impact on morbidity and mortality, with mixed results reported. Furthermore, the limited data on the efficacy of ERAS in minimally invasive cardiac surgeries makes it difficult to establish well-supported guidelines for these procedures. Despite its limitations, the overall outcomes of ERAS for cardiac surgery remain promising. As our understanding and application of ERAS in cardiac surgery continue to evolve, these protocols have the potential to redefine cardiac surgical care standards.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it