Comparison between minimally invasive and open transforaminal lumbar interbody fusion for the treatment of multi‑segmental lumbar degenerative disease: A systematic evaluation and meta‑analysis
Bibliographic record
Abstract
The present study aimed to compare the differences between minimally invasive transforaminal lumbar fusion (MIS-TLIF) and open transforaminal lumbar fusion (TLIF) for multi-segmental lumbar degenerative disease regarding intraoperative indices and postoperative outcomes. PubMed, Web of Science, Embase, CNKI, Wanfang and VIP databases were searched for literature on MIS-TLIF and open TLIF in treating multi-segmental lumbar degenerative diseases. Of the 1,608 articles retrieved, 10 were included for final analysis. The Newcastle-Ottawa Scale and Review Manager 5.4 were used for quality evaluation and data analysis, respectively. The MIS-TLIF group was superior to the open TLIF group regarding intraoperative blood loss [95% confidence interval (CI): -254.33,-157.86; P<0.00001], postoperative in-bed time (95%CI: -3.49,-2.76; P<0.00001), hospitalization time (95%CI: -5.14,-1.78; P<0.0001) and postoperative leg pain Visual Analog Scale score (95%CI: -0.27,-0.13; P<0.00001). The fluoroscopy frequency for MIS-TLIF (95%CI: 2.07,6.12; P<0.0001) was significantly higher than that for open TLIF. The two groups had no significant differences in operation time, postoperative drainage volume, postoperative complications, fusion rate, or Oswestry Disability Index score. In treating multi-segmental lumbar degenerative diseases, MIS-TLIF has the advantages of less blood loss, shorter bedtime and hospitalization time and improved early postoperative efficacy; however, open TLIF has a lower fluoroscopy frequency.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".