Comparative efficacy of unilateral biportal endoscopy and micro-endoscopic discectomy in the treatment of degenerative lumbar spinal stenosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Given the inconclusive literature on operative time, pain relief, functional outcomes, and complications, this meta-analysis aims to compare the efficacy of Unilateral Biportal Endoscopy (UBE) and Micro-Endoscopic Discectomy (MED) in treating Degenerative Lumbar Spinal Stenosis (DLSS). METHODS: A thorough literature search was conducted in accordance with the PRISMA guidelines and based on the PICO framework. The study interrogated four primary databases-PubMed, Embase, Web of Science, and the Cochrane Library-on August 16, 2023, without time restrictions. The search employed a strategic selection of keywords and was devoid of language barriers. Studies were included based on strict criteria, such as the diagnosis, surgical intervention types, and specific outcome measures. Quality assessment was performed using the Newcastle-Ottawa Scale, and statistical analysis was executed through Stata version 17. RESULTS: The meta-analysis incorporated 9 articles out of an initial yield of 1,136 potential studies. Considerable heterogeneity was observed in surgical duration, but no statistically significant difference was identified (MD = - 2.11, P = 0.56). For VAS scores assessing lumbar and leg pain, UBE was statistically superior to MED (MD = - 0.18, P = 0.013; MD = - 0.15, P = 0.006, respectively). ODI scores demonstrated no significant difference between the two surgical methods (MD = - 0.57, P = 0.26). UBE had a lower incidence of complications compared to those receiving MED (OR = 0.54, P = 0.036). CONCLUSIONS: UBE and MED exhibited comparable surgical durations and disability outcomes as measured by ODI. However, UBE demonstrated superior efficacy in alleviating lumbar and leg pain based on VAS scores. The findings present an intricate evaluation of the two surgical interventions for DLSS, lending valuable insights for clinical decision-making.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".