Minimally invasive versus open surgery for cervical and lumbar discectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Minimally invasive surgery for discectomy may accelerate recovery and reduce pain, but it also requires technical expertise and is associated with increased risks. We performed a meta-analysis to determine the effects of minimally invasive versus open surgery on functional outcomes, pain, complications and reoperations among patients undergoing cervical or lumbar discectomy. METHODS: We searched MEDLINE, Embase and the Cochrane Library for reports of relevant randomized controlled trials published to Jan. 12, 2014. Two reviewers assessed the eligibility of potential reports and the risk of bias of included trials. We analyzed functional outcomes and pain using standardized mean differences (SMDs) that were weighted and pooled using a random-effects model. RESULTS: We included 4 trials in the cervical discectomy group (n = 431) and 10 in the lumbar discectomy group (n = 1159). Evidence overall was of low to moderate quality. We found that minimally invasive surgery did not improve long-term function (cervical: SMD 0.11, 95% confidence interval [CI] -0.09 to 0.31; lumbar: SMD 0.04, 95% CI -0.11 to 0.20) or reduce long-term extremity pain (cervical: SMD -0.21, 95% CI -0.52 to 0.10; lumbar: SMD 0.08, 95% CI -0.16 to 0.32) compared with open surgery. The evidence suggested overall higher rates of nerve-root injury (risk ratio [RR] 1.62, 95% CI 0.45 to 5.84), incidental durotomy (RR 1.56, 95% CI 0.80 to 3.05) and reoperation (RR 1.48, 95% CI 0.97 to 2.26) with minimally invasive surgery than with open surgery. Infections were more common with open surgery than with minimally invasive surgery (RR 0.24, 95% CI 0.04 to 1.38), although the difference was not statistically significant. INTERPRETATION: Current evidence does not support the routine use of minimally invasive surgery for cervical or lumbar discectomy. Well-designed trials are needed given the lack of high-quality evidence.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".