Expansive Suspension Laminoplasty Using a Spinous Process–Splitting Approach for Lumbar Spinal Stenosis: Surgical Technique and Outcomes Over 8 Years of Follow-up
Bibliographic record
Abstract
INTRODUCTION: To maximize the benefits of posterior decompression for severe multilevel lumbar spinal stenosis, we refined the expansive laminoplasty technique using a spinous process-splitting approach. This study tests the hypothesis that the surgical benefit of adequate decompression with posterior element preservation is maintained in the long term, over 8 years of follow-up. METHODS: Fifty-eight patients were followed up yearly for 8 years. Eight patients having nonlumbar spine surgery or Parkinson disease were excluded. The noninferiority of the 8-year versus peak-year outcomes was tested, with margins of 5 points for the Oswestry disability index and 1 point for the numeric rating scales (NRSs). RESULTS: In the 50 patients available for follow-up, the peak values of the mean improvements from baseline within the first 7 years were 35.8, 5.7, 5.9, and 2.8 points for the Oswestry disability index, low back pain NRS, leg pain NRS, and leg numbness NRS, respectively. The 95% lower confidence limits for the differences between the mean improvements from baseline at 8 years and the peak year were within the noninferiority margins for each scale. CONCLUSION: Our technique was associated with substantial improvement from baseline for each scale. The initial improvements in function and symptoms were maintained for 8 years.
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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 | 0.001 |
| 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 | 0.000 |
| 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".