Surgical Data and Early Postoperative Outcomes after Minimally Invasive Lumbar Interbody Fusion: Results of a Prospective, Multicenter, Observational Data-Monitored Study
Bibliographic record
Abstract
UNLABELLED: Minimally invasive lumbar interbody fusion (MILIF) offers potential for reduced operative morbidity and earlier recovery compared with open procedures for patients with degenerative lumbar disorders (DLD). Firm conclusions about advantages of MILIF over open procedures cannot be made because of limited number of large studies of MILIF in a real-world setting. Clinical effectiveness of MILIF in a large, unselected real-world patient population was assessed in this Prospective, monitored, international, multicenter, observational study. OBJECTIVE: To observe and document short-term recovery after minimally invasive interbody fusion for DLD. MATERIALS AND METHODS: In a predefined 4-week analysis from this study, experienced surgeons (≥ 30 MILIF surgeries pre-study) treated patients with DLD by one- or two-level MILIF. The primary study objective was to document patients' short-term post-interventional recovery (primary objective) including back/leg pain (visual analog scale [VAS]), disability (Oswestry Disability Index [ODI]), health status (EQ-5D) and Patient satisfaction. RESULTS: At 4 weeks, 249 of 252 patients were remaining in the study; the majority received one-level MILIF (83%) and TLIF was the preferred approach (94.8%). For one-level (and two-level) procedures, surgery duration was 128 (182) min, fluoroscopy time 115 (154) sec, and blood-loss 164 (233) mL. Time to first ambulation was 1.3 days and time to study-defined surgery recovery was 3.2 days. Patients reported significantly (P < 0.0001) reduced back pain (VAS: 2.9 vs 6.2), leg pain (VAS: 2.5 vs 5.9), and disability (ODI: 34.5% vs 45.5%), and a significantly (P < 0.0001) improved health status (EQ-5D index: 0.61 vs 0.34; EQ VAS: 65.4 vs 52.9) 4 weeks postoperatively. One adverse event was classified as related to the minimally invasive surgical approach. No deep site infections or deaths were reported. CONCLUSIONS: For experienced surgeons, MILIF for DLD demonstrated early benefits (short time to first ambulation, early recovery, high patient satisfaction and improved patient-reported outcomes) and low major perioperative morbidity at 4 weeks postoperatively.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".