P.233 Implementation of an Enhanced Recovery After Surgery (ERAS) Protocol for Scheduled Spine Surgery
Bibliographic record
Abstract
Background: Enhanced Recovery After Surgery (ERAS) Protocols improve post-surgical outcomes through decreased length of hospital stay, reduced readmission rates, decreased post-operative pain, and greater patient satisfaction. ERAS also has significant benefits to the healthcare system through reduced cost of post-operative care. While ERAS protocols are well established in many surgical fields, a complete guideline for spine surgery is lacking. Early ERAS studies in spine surgery suggest up to a 50% reduction in length of stay (LOS) and decreased cost of care. Methods: Primary literature review followed by multidisciplinary critical appraisal for optimization and redesign of our current system of care for scheduled spine surgery (SSS), including patient experience and team logistics from initial consultation through post-operative care and follow up. Results: An evidence-based guideline, optimizing pre-, intra-, and post-operative phases of care was developed. Specific focus catered to pre-operative education and patient barriers to discharge. Further improvements in pre-admission patient goal setting, introduction of a patient care “passport”, post-operative reduction in narcotic administration, and increased same day post-operative mobilization were means to reduce LOS. Conclusions: A spine ERAS pathway was developed, allowing our care program to better facilitate patient recovery after SSS. Future work will aim to determine economic impact of the pathway.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".