Lumbar spinal epidural lipomatosis
Bibliographic record
Abstract
INTRODUCTION: Lumbar spinal epidural lipomatosis (SEL) is a rare condition defined by an excessive deposition of adipose tissue in the lumbar spinal canal. The objective of this case report is to document a clinical case of SEL presenting within a multidisciplinary spine clinic and to compare our clinical findings and management with the available literature. CASE PRESENTATION: A 51-year-old female presented at a spine clinic with low back pain, bilateral leg pain and difficulty walking. Magnetic resonance imaging of the lumbar spine showed evidence of severe central canal stenosis due to extensive epidural lipomatosis. She was initially advised to lose weight and undergo a 3-month course of physiotherapy. However, because of lack of improvement, she was scheduled for and underwent L4-S1 posterior spinal decompression and L4-L5 posterior spinal instrumented fusion. At 12-month follow-up, the patient reported no pain and retained the ability to walk regular distances without experiencing discomfort. DISCUSSION: This case report describes the conservative and surgical management of a case of lumbar spinal stenosis due to SEL. The therapeutic approach of patients with this condition is not standardized. As such, a discussion of the literature with respect to the diagnosis, clinical presentation, epidemiology, imaging appearance, risk factors, etiology, and management of SEL is also presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".