Sagittal Balance Correction Following Lumbar Interbody Fusion: A Comparison of the Three Approaches
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort study. PURPOSE: The objective of this study was to compare three widely used interbody fusion approaches in regard to their ability to correct sagittal balance, including pelvic parameters. OVERVIEW OF LITERATURE: Restoration of sagittal balance in lumbar spine surgery is associated with better postoperative outcomes. Various interbody fusion techniques can help to correct sagittal balance, with no clear consensus on which technique offers the best correction. METHODS: The charts and imaging of patients who have undergone surgery through either open transforaminal lumbar interbody fusion (TLIF), minimally invasive TLIF (MIS TLIF), or oblique lumbar interbody fusion (OLIF) were retrospectively reviewed. The following sagittal balance parameters were measured pre- and postoperatively: segmental lordosis, lumbar lordosis, disk height, pelvic tilt, and pelvic incidence. Data on postoperative complications were gathered. RESULTS: Only OLIF managed to significantly improve segmental lordosis (4.4°, p <0.001) and lumbar lordosis (4.8°, p =0.049). All approaches significantly augmented disk height, with OLIF having the greatest effect (3.7°, p <0.001). No approaches were shown to significantly correct pelvic tilt. Pelvic incidence remained unchanged in all approaches. Open TLIF was the only approach with a higher rate of postoperative complications (33%, p =0.009). CONCLUSIONS: The OLIF approach might offer greater correction of sagittal balance over open and MIS TLIF, mainly in regard to segmental lordosis, lumbar lordosis, and disk height. MIS TLIF, although offering more limited access than open TLIF, was not inferior to open TLIF in regard to sagittal balance correction. A higher rate of complications was shown for open TLIF than the other approaches, possibly due to its more invasive nature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".