Minimally Invasive versus Open Thoracolumbar Surgery for Lumbar Spinal Stenosis in Patients with Diabetes: A CSORN Study
Bibliographic record
Abstract
Introduction: This study was aimed at comparing outcomes of minimally invasive (MIS) versus OPEN surgery for lumbar spinal stenosis (LSS) in patients with diabetes. Methodology: This retrospective cohort study included patients with diabetes who underwent spinal decompression alone or with fusion for LSS within the Canadian Spine Outcomes and Research Network (CSORN) database. Outcomes of MIS and OPEN approaches were compared for two cohorts: (i) patients with diabetes who underwent decompression alone (N = 116; MIS, n = 58, OPEN, n = 58) and (ii) patients with diabetes who underwent decompression with fusion (N = 108; MIS, n = 54, OPEN, n = 54). Mixed measures analyses of covariance compared modified Oswestry Disability Index (mODI) and back and leg pain at one-year post operation. The number of patients meeting minimum clinically important difference (MCID) or minimum pain/disability at one year were compared. Result: MIS approaches had less blood loss (decompression alone difference 99.66 mL, p = 0.002; with fusion difference 244.23, p < 0.001) and shorter LOS (decompression alone difference 1.15 days, p = 0.008; with fusion difference 1.23 days, p = 0.026). MIS compared to OPEN decompression with fusion had less patients experience an adverse event (difference, 13 patients, p = 0.007). The MIS decompression with fusion group had lower one-year mODI (difference, 14.25, p < 0.001) and back pain (difference, 1.64, p = 0.002) compared to OPEN. More patients in the MIS decompression with fusion group exceeded MCID at one year for mODI (MIS 75.9% vs OPEN 53.7%, p = 0.028) and back pain (MIS 85.2% vs OPEN 70.4%, p = 0.017). Conclusion: MIS approaches were associated with more favorable outcomes for patients with diabetes undergoing decompression with fusion for LSS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".