Minimally invasive transforaminal lumbar interbody fusion versus oblique lateral interbody fusion for lumbar degenerative disease: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) and oblique lateral interbody fusion (OLIF) are widely used in the treatment of lumbar degenerative diseases. In the present study, a meta-analysis was conducted to compare the clinical and radiographic efficacy of these two procedures. METHODS: A systematic literature review was performed, and the quality of retrieved studies was evaluated with the Newcastle-Ottawa Scale (NOS). Clinical outcomes, including operation time, intraoperative blood loss, improvement in Visual Analogue Scale (VAS), improvement in Oswestry Disability Index (ODI), Japanese Orthopaedic Association Back Pain Evaluation Questionnaire (JOABPEQ) effectiveness rate and complications, in addition to radiographic outcomes, including restoration of disc height, disc angle, overall lumbar lordosis, fusion rate and subsidence, were extracted and input into a fixed or random effect model to compare the efficacy of MIS-TLIF and OLIF. RESULTS: Seven qualified studies were included. Clinically, OLIF resulted in less intraoperative blood loss and shorter operation time than MIS-TLIF. Improvement of VAS for leg pain was more obvious in the OLIF group (P < 0.0001), whereas improvement of VAS for back pain (P = 0.08) and ODI (P = 0.98) as well as JOABPEQ effectiveness rate (P = 0.18) were similar in the two groups. Radiographically, OLIF was more effective in restoring disc height (P = 0.01) and equivalent in improving the disc angle (P = 0.18) and lumbar lordosis (P = 0.48) compared with MIS-TLIF. The fusion rate (P = 0.11) was similar in both groups, while the subsidence was more severe in the MIS-TLIF group (P < 0.00001). CONCLUSIONS: The above evidence suggests that OLIF is associated with a shorter operation time (with supplementary fixation in the prone position) and less intraoperative blood loss than MIS-TLIF and can lead to better leg pain alleviation, disc height restoration and subsidence resistance. No differences regarding back pain relief, functional recovery, complications, disc angle restoration, lumbar lordosis restoration and fusion rate were found. However, due to the limited number of studies, our results should be confirmed with high-level studies to fully compare the therapeutic efficacy of MIS-TLIF and OLIF. TRIAL REGISTRATION: PROSPERO ID: CRD42020201903 .
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.005 | 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) | 0.003 | 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".