Oblique versus Transforaminal Lumbar Interbody Fusion in Degenerative Spondylolisthesis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: This meta-analysis compared transforaminal interbody fusion (TLIF) and oblique lumbar interbody fusion (OLIF) techniques for degenerative lumbar spondylolisthesis. AIM: The aim is to evaluate Oswestry Disability Index (ODI), Japanese Orthopedic Association Back Pain Evaluation Questionnaire, visual analog scale improvement for back and leg pain, disc height, slipped percentage, blood loss, surgical time, and complication rates in both groups. METHODS: A systematic literature search was carried out to obtain a study that compared OLIF and TLIF for degenerative lumbar spondylolisthesis. A literature search was performed using PubMed, Scopus, EuropePMC, and EBSCOHost. While the intervention was the OLIF technique, the control was the TLIF technique. The primary outcome was clinical outcome (ODI, Japanese Orthopaedic Association Back Pain Evaluation Questionnaire [JOABPEQ], visual analog scale [VAS] improvement for back, and leg pain). The Newcastle-Ottawa Scale was used to assess the quality of the studies. RESULTS: Total of 384 patients from four studies were included in this study. OLIF group was better than TLIF group in terms of disc height, slipped percentage, and blood loss. ODI, JOABPEQ, VAS improvement for back pain (standardized mean difference [SMD] 0.06 [−0.18, 0.29], p = 0.63, I2 = 0%, p = 0.87) and leg pain (SMD 0.12 [−0.36, 0.60], p = 0.63, I2 = 74%, p = 0.02), surgical time, and complication rates were similar in both groups. CONCLUSION: OLIF technique was better than TLIF technique in terms of radiologic outcome and surgical blood loss. Both techniques showed similar outcomes in clinical outcome, complication, and surgical time.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| 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 | 0.001 |
| 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".