Long-Term Outcomes of Laminectomy in Lumbar Spinal Stenosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Objective Lumbar spinal stenosis (LSS) patients suffer from significant pain and disability. To assess long-term safety and efficacy of laminectomy in LSS patients, a systematic review and meta-analysis study was conducted. Methods Literature review in MEDLINE, Embase, Scopus, Web of Science, and Cochrane Library databases was performed using a predefined search strategy. Articles were included if they met the following characteristics: human studies, LSS, and at least 5 years of follow-up. Outcome measures included patient satisfaction, pain, disability, claudication, reoperation rates, and complications. Results Twelve articles met the eligibility criteria for our study. Overall, there was low-quality evidence that patients undergoing laminectomy, with at least 5 years of follow-up, have significantly more satisfaction, and less pain and disability, compared with the preoperative baseline. Assessment of neurogenic intermittent claudication showed significant improvement in walking abilities. We also reviewed the postoperative complication and adverse events in the included studies. After meta-analysis was performed, the reoperation rate was found to be 14% (95% confidence interval: 13–16%). Conclusion Our study provides low-quality evidence suggesting that patients undergoing laminectomy for LSS have less disability and pain and can be more physically active postoperatively.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".