Anterior Lumbar Interbody Fusion for Treatment of Failed Back Surgery Syndrome: An Outcome Analysis
Bibliographic record
Abstract
OBJECTIVE: Anterior lumbar interbody fusion (ALIF) has gained popularity for the treatment of degenerative disease of the lumbar spine. In this report, we present our experience with the ALIF procedure for treatment of failed back surgery syndrome (FBSS) in a noncontrolled prospective cohort. METHODS: In a 2-year period, we treated patients diagnosed with FBSS with ALIF. Clinical and radiological outcomes were recorded in a prospective, nonrandomized, longitudinal manner. Neurological, pain, and functional outcomes were measured preoperatively and 12 months after surgery. Operative data, perioperative complications, and radiological and clinical outcomes were recorded. RESULTS: Thirty-three patients with a preoperative diagnosis of FBSS, with degenerative disc disease (n = 17), postsurgical spondylolisthesis (n = 13), or pseudarthrosis (n = 3), underwent ALIF. Back pain, leg pain, and functional status improved significantly, by 76% (P < 0.01), 80% (P < 0.01), and 67% (P < 0.01), respectively. CONCLUSION: On the basis of our results, we found ALIF to be a safe and effective procedure for the treatment of FBSS for selected patients.
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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) | 0.000 | 0.001 |
| 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".