Complications in Minimally Invasive Spine Surgery (2013–2024): Lumbar Spine—Tubular Minimally Invasive Techniques
Bibliographic record
Abstract
STUDY DESIGN: Systematic review and proportional meta-analysis. OBJECTIVE: To quantify overall and specific complication rates associated with tubular minimally invasive spine surgery (MISS) for lumbar pathologies over the past decade. SUMMARY OF BACKGROUND DATA: Tubular MISS is widely used for lumbar pathologies due to its reduced tissue disruption and faster recovery compared with open surgery. However, reported complication rates vary, and pooled estimates for specific complications remain limited. MATERIALS AND METHODS: A systematic search of PubMed, Medline, Embase, and the Cochrane Library (January 2013-March 2024) was conducted following PRISMA guidelines. Studies were included if they involved 10 adult patients undergoing tubular lumbar MISS and provided extractable complication data. A random-effects model was used to pool complication rates, and study quality was assessed using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. All analyses were done using R studio. RESULTS: Seventy-five studies involving ∼12,600 patients were included in the analysis. The complication rate was 10% (95% CI: 8%-14%, I2=93%). Specific complication rates were: dural tears 4% (95% CI: 3%-5%, I2=69%) in 56 studies (6651 patients); nerve injuries 1% (95% CI: 1%-3%, I2=70%) in 41 studies (5278 patients); postoperative hematoma 1% (95% CI: 1%-2%, I2=31%) in 19 studies (2454 patients); surgical site infections 1% (95% CI: 0%-1%, I2=27%) in 46 studies (10,439 patients); revision surgeries 2% (95% CI: 2%-3%, I2=77%) in 43 studies (8948 patients); and disc reherniation 3% (95% CI: 1%-7%, I2=84%) in 14 studies (1928 patients). CONCLUSION: This meta-analysis provides a comprehensive overview of complication rates in tubular lumbar MISS, revealing generally low rates but significant heterogeneity across studies. These findings offer valuable insights for patient counseling and surgical planning, though individual patient factors and surgeon experience should be considered.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".