Comprehensive guidelines for prehabilitation in spine surgery
Bibliographic record
Abstract
Study Design: Literature review. Objectives: Review prehabilitation techniques used for elective spine surgery to create a comprehensive list of recommendations. Methods: A systematic review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines by searching three electronic databases from 1997 to 2021. Pertinent literature reporting information on prehabilitation, applicable to spine surgery, was identified. Seventy studies were selected for further analysis. Findings from the literature were reinforced by practices employed by the authors. Results: may be a viable solution to minimize wound complications and surgical site infections. To minimize the negative impact of cardiopulmonary comorbidities, patients can enroll in an exercise program prior to surgery. Patients should abstain from alcohol before elective spine surgery. Patients with osteoporosis may benefit from supplementation with Vitamin D, calcium, and parathyroid hormone. Opioids should be weaned to complete cessation 6-8 weeks before surgery. Preoperative cognitive behavioral therapy (CBT) and education seem to be the most beneficial in reducing complications associated with psychiatric comorbidities. Patients should engage in a comprehensive prehabilitation regimen. Conclusion: Targeting patient risk factors with personalized interventions can improve postoperative outcomes in patients undergoing elective spine surgery.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".