Analysis of Spinopelvic Sagittal Balance and Persistent Low Back Pain (PLBP) for Degenerative Spondylolisthesis (DS) following Posterior Lumbar Interbody Fusion (PLIF)
Bibliographic record
Abstract
Objective: To investigate the change of spinopelvic sagittal balance and clinical outcomes after posterior lumbar interbody fusion (PLIF) in patients with degenerative spondylolisthesis (DS), especially the relationship between sagittal spinopelvic parameters and persistent low back pain (PLBP). Methods: 107 patients who were diagnosed with DS and underwent PLIF in our department were enrolled retrospectively in the present study. Sagittal spinopelvic parameters including lumbar lordosis (LL), segmental lordosis (SL), height of the disc (HOD), sacral slope (SS), pelvic incidence (PI), and pelvic tilt (PT) were recorded pre- and postoperatively. Sagittal balance and clinical outcomes were compared between patients with and without PLBP. Pearson correlation was used to analyze the change of sagittal balance parameters and clinical functions. Logistic regression analysis was performed to examine the risk factors of PLBP. Results: It showed significant improvements of SL, HOD, and PT postoperatively. Both the Numeric Rating Scale (NRS) and Oswestry Disability Index (ODI) had significant improvement postoperatively. Change of PT and SL also differed observably between patients with and without PLBP. SL and PT were correlated with NRS and ODI, and insufficient restoration of PT was an independent factor for PLBP. Conclusion: The sagittal balance parameters and clinical outcomes can be improved markedly via PLIF for treating DS. Restoration of SL and PT was correlated with satisfactory outcomes, and adequate improvement of PT may have positive impact on reducing PLBP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".