Which is the optimum surgical strategy for spondylolisthesis: Reduction or fusion in situ? A meta-analysis from 12 comparative studies
Bibliographic record
Abstract
PURPOSE: To compare the clinical outcomes and complications and radiographic outcomes of the two different surgical strategies (arthrodesis in situ and arthrodesis following reduction) for the surgical management of spondylolisthesis. METHODS: After systematic search the PubMed, Ovid MEDLINE, Cochrane, and Embase databases, comparative studies were selected according to eligibility criteria. Checklists by Furlan and by The Newcastle-Ottawa quality assessment scale (NOS scale) were used to evaluate the risk of bias of the included randomized clinical trials (RCTs) and nonrandomized controlled studies, respectively. The final strength of evidence was expressed as different levels recommended by the GRADE Working Group. RESULTS: Three RCTs. and nine comparative observational studies were identified. Low-quality evidence indicated that reduction group (RG) was not more effective than fusion in situ group for clinical satisfaction (OR 0.77, 95% CI 0.39-1.54, P = 0.46). and neurologic complication rate (OR 0.89, 95 CI 0.38-2.03, P = 0.78). In secondary outcomes, Low-quality evidence indicated that RG improved fusion rate (OR 2.66, 95% CI 1.15-6.14, P = 0.02). There was no significant difference in the other complication rate (OR 0.89, 95% CI 0.44-1.79, P = 0.63) and blood loss (WMD 14.22, 95% CI -9.53-37.79, P = 0.24) between two groups. Statistical difference was found between the two groups with regard to slipping angle (WMD -6.33, 95% CI -12.60 to -0.06, P = 0.05). CONCLUSIONS: There was no definite benefit of reduction over fusion in situ in clinical satisfaction rate and neurologic complication rate. The fusion rate significantly improved while the slipping angle considerably decreased postoperation in reduction group.
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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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.059 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".