Surgical Outcomes After Single-Level Endoscopic Transforaminal Lumbar Interbody Fusion: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and objective Novel surgical advancements have introduced endoscopic operative techniques for low back surgery, including transforaminal lumbar interbody fusion (TLIF), which theoretically allows for improved decompression with minimal invasiveness. In addition, endoscopically performed TLIF has allowed for the use of local anesthesia as an alternative method to general anesthesia for patients. We aimed to evaluate the clinical outcomes in patients undergoing endoscopic TLIF and also compare the outcomes in patients undergoing general versus local anesthesia. Methods The databases of PubMed, Medline, Embase, and the Cochrane Library were queried for all studies involving patients undergoing endoscopic TLIF. After the extraction of the data and assessment of study quality via the Newcastle-Ottawa Scale, statistical analysis was performed with the R software (The R Foundation, Vienna, Austria) metafor package. The random-effects model was used as the data was largely heterogeneous (I2 >50%). Results In total, 15 studies involving a total of 441 patients were selected for the final quantitative meta-analysis. The overall mean difference between the postoperative visual analog scale (VAS) leg scores and preoperative VAS scores was 3.45 (95% CI: 4.93-1.97, p: <0.01). Postoperative VAS low back scores revealed a mean difference of 3.36 (95% CI: 5.09-1.63, p: <0.01). The overall mean difference of ODI scores was 4.58 (95% CI: 6.76-2.40, p: <0.01). Mean blood loss was 136.32 mL and the mean operative time was 149.15 minutes. The mean length of stay postoperatively was lower in the local anesthesia group compared to the general anesthesia group (1.40 vs 5.99 days respectively). There were no outcome variables of patients undergoing general anesthesia versus local anesthesia that showed statistically significant differences in this analysis due to the small amount of data published on patients undergoing endoscopic TLIF with local anesthesia. In addition, the failure of studies in reporting standard deviations as data parameters further limited the quantitative analysis. Conclusion Endoscopic TLIF appears to be a viable option for patients undergoing lumbar interbody fusion. Initial data reveal that endoscopic TLIF with local anesthesia may offer patients outcomes similar to those in patients undergoing endoscopic TLIF with general anesthesia, with lower operative times and length of stay.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".