Radiographic Predictors of Functional Outcome in Degenerative Lumbar Spondylolisthesis Surgery
Bibliographic record
Abstract
Objective: To confirm the importance of sagittal spinal alignment on functional outcome with degenerative lumbar spondylolisthesis (DLS) surgery and to identify the radiographic parameters that predict functional outcomes after DLS surgery.\nMethods: Retrospective analysis of the prospectively collected functional and radiographic outcomes of the Canadian Spine Outcomes and Research Network DLS database. All patients underwent either decompression, posterolateral fusion or interbody fusion surgery with a minimum of one-year postoperative follow-up.\nResults: Most patients improve or remain unchanged in their sagittal spinal alignment regardless of surgery type with fusion procedures not experiencing statistically significantly improved alignment changes to decompression alone. By multiple linear regression adjusted for baseline patient age, body mass index, gender and preoperative presence of depression, worsening of a patient’s pelvic incidence-lumbar lordosis (LL) mismatch with any technique of DLS surgery was associated with a higher one-year postoperative ODI score R2 0.179 (95% CI 0.080, 0.415, p=0.004), back pain R2 0.152 (95% CI 0.021, 0.070, p 2 0.059 (95% CI 0.008, 0.066, p=0.014) score. Likewise, reduction of LL was associated with a higher ODI score R2 0.168 (-0.387, -0.024, p=0.027) and back pain R2 0.135 (95% CI -0.064, -0.010, p=0.007).\nConclusions: This is the first work to examine DLS patients outside of extrapolated sagittal balance parameters from the adult scoliosis literature. Importantly, we show that any worsening in sagittal spinal alignment parameters with DLS surgery regardless of surgery type leads to poorer functional outcomes even among patients who remain within conventionally held appropriate sagittal balance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".