Is Indirect Decompression and Fusion More Effective than Direct Decompression and Fusion for Treating Degenerative Lumbar Spinal Stenosis With Instability? A Systematic Review and meta-Analysis
Bibliographic record
Abstract
Study design: Systematic Review and Meta-analysis. Objective: Surgical alternatives to treat lumbar spinal stenosis and instability include indirect (ALIF, OLIF, and LLIF) and direct (TLIF or posterior lumbar interbody fusion) decompression and fusion interventions. Although both approaches have proven to be effective in reducing symptoms, it is unknown if there is any difference in effectiveness between them. In this systematic review and meta-analysis, we aimed to evaluate postoperative pain and disability in patients treated whit indirect vs direct decompression and fusion approaches. Methods: We conducted a systematic review of the literature consulting several databases and identified studies that enrolled patients diagnosed with degenerative lumbar spinal stenosis and instability treated with indirect or direct decompression and fusion techniques. Our primary endpoints were the visual analogue scale, Oswestry Disability Index, and the Japanese Orthopedics Association Back Pain Evaluation Questionnaire 1 year after the procedure. Secondary outcomes included complication rate, blood loss, and surgical time. Results: Nine retrospective and comparative studies were included enrolling a total of 1004 participants. Both surgical strategies had satisfactory clinical outcomes with no significant difference at 1 year. Although the complication rate was similar for both groups, the profile of the adverse events was different. In addition, patients treated with indirect decompression and fusion had significantly less blood loss and operative times. Conclusions: Indirect and direct decompression and fusion techniques are similarly effective in treating patients with lumbar spinal stenosis and instability. The ID group had significantly lower intraoperative blood loss and surgical time values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".