Enhanced Recovery After Surgery (ERAS) in Spine Surgery: A Systematic Review and Meta-Analysis of Spinal Surgery Sub – Specialities, Interventions and Efficacy
Bibliographic record
Abstract
Study Design Systematic Review. Objectives Enhanced Recovery After Surgery (ERAS) is a widely acknowledged approach for improving surgical outcomes. This review aims at analyzing the characteristics of study populations, interventions and outcomes in spine patients. Methods Embase and Ovid were searched from inception until March 2025. We followed PRISMA guidelines. Study quality and risk of bias were assessed. In addition to a narrative synthesis of the evidence, a meta-analysis of RCTs evaluating length of stay (LOS) for a lumbar spine population was conducted. This review was registered prospectively on PROSPERO (No. CRD42025638293). Results 1431 records were identified, from which 81 studies were included. Reports of ERAS predominantly exist for degenerative spine pathologies (n = 35/81, 43.2%) and spinal deformities (n = 29/81, 35.8%). Most studied interventions were postoperative analgesia, early mobilisation (both n = 61/81, 75.3%) and patient education (n = 60, 74.1%). The most frequently used outcome measures were LOS (n = 65/81, 92.9%) and complication rates (n = 40/81, 49.4%). The overall median complication rate for ERAS patients was found to be lower (8.8% vs 15.6%). There was a statistically non-significant tendency for ERAS shortening LOS for 1 day in lumbar spine patients [95%CI -2.77, 0.71; P = 0.25]. Conclusions ERAS in spine surgery appears to be effective in terms of reducing LOS and complication rates. Further efforts at refining pain management and targeted disease-specific interventions are required. Whether ERAS interventions applied to individuals with significant neurological impairment and/or medical frailty, can influence surgical outcomes needs to be further studied.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".