Effects of prehabilitation on outcomes following elective lumbar spine surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Elective lumbar spine surgery is increasingly being implemented to treat patients with specific low back pain. However, approximately 30% of patients continue to have long-term pain and disability after surgery. Objective: The aim of this study was to systematically review the literature on the effectiveness of pre-surgical rehabilitation (prehab) alone or in combination with usual care versus usual care on patient-oriented outcomes and health-related costs following elective lumbar spine surgery. Data sources: Electronic databases from MEDLINE, CINAHL, EMBASE, and AMED were systematically searched from their inception to November 2022. Study selection: Randomized controlled trials that examined adult (age >18 years) prehab programs and evaluated one or more outcomes of interest were included in this review. Data extraction: In pairs, six reviewers independently conducted a risk-of-bias assessment and extracted outcome data from included studies, in accordance with the Template for Intervention Description and Replication (TIDieR). A meta-analysis was conducted when trials were homogeneous. Data synthesis: = 739 participants), reported in 13 different manuscripts, were eligible for inclusion. Exercise prehab interventions are superior to usual care for disability at 3-month (MD: -2.56, 95% CI -4.98 to -0.15), back pain at 6-month (MD: -6.65, 95% CI -13.25 to -0.05), and health-related costs (MD: €2572.8, 95% CI: €1963.0 to €3182.5). CBT prehab interventions seem to be superior to usual care for back pain at 3-month (MD: -7.3, 95% CI: -14.5 to -0.05). Individual trials showed that education prehab interventions may be superior to usual for back pain at 1-month post-operative (MD: 12.3, 95% CI: 0.9 to 23.7). Limitations: Overall, the inclusion of heterogeneous trials (e.g., diagnosis, types of surgery, dosage, content, and duration of interventions) with small sample sizes leads to inconclusive and very low certainty of effect estimates. Conclusion: The present systematic review has brought to light the dearth of high-quality evidence in support of prehab interventions for patients undergoing lumbar spine surgery. Given the uncertainty surrounding the results obtained from low-quality randomized controlled trials, it is currently not feasible to provide recommendations for clinical practice.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".