P.131 Lumbar fusion for degenerative disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Lumbar fusion for degenerative indications is associated with a great degree of practice variation. We summarize the current evidence on the comparative safety and efficacy of lumbar fusion, decompression alone, or non-operative care for degenerative indications. Methods: Literature search of electronic bibliographic databases was conducted. Comparative studies reporting validated measures of safety or efficacy were included. Treatments effects were calculated through DerSimonian and Laird random effects models. Results: We retrieved 62 studies (17 randomized controlled, 15 prospective, 15 retrospective, and 15 registries), enrolling a total 302,347 adult patients. Disability, pain, and patient satisfaction following fusion, decompression alone, or non-operative care were dependent on surgical indications and study methodology. Relative to decompression alone, the risk of reoperation following fusion was increased for spinal stenosis (relative risk [RR] 1.17, 95% CI 1.06 to 1.30, p<0.004) and decreased for spondylolisthesis (RR 0.71, 95% CI 0.59 to 0.84, p<0.001). In all indications, complications were more frequent following fusion (RR 1.88, 95% CI 1.37 to 2.58, p<0.001). Mortality and treatment modality were not associated. Conclusions: Improvements were greatest in patients undergoing fusion for spondylolisthesis while complications limited the role of fusion for spinal stenosis. The relative safety and efficacy of fusion for chronic low back pain suggested careful patient selection is required.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".