The Role of Sacrolumbar Fat Grafting in the Treatment of Spinal Fusion Instrumentation-Related Chronic Low Back Pain
Bibliographic record
Abstract
In Brief Study Design. A report of 2 cases. Objective. The purpose of this article is to report 2 preliminary cases with instrumentation-related chronic low back pain (CLBP) successfully treated with fat graft in the sacrolumbar region. Summary of Background Data. Patients undergoing successful spinal fusion surgery may experience new or recurrent CLBP. Instrumentation-related soft-tissue irritation is a well-known etiology of this frustrating condition. Treatment options vary from conservative treatment till instrumentation removal, with no consensus on their efficacy. Methods. A 32-year-old patient and a 37-year-old patient with instrumentation-related debilitating CLBP visual analogue scale score 7 and 10, respectively, underwent 1 session of fat grafting in the sacrolumbar region. Results. At 9-month and 6-month follow-ups, both patients reported a substantial pain relief, a considerable improvement in daily quality of life and satisfaction for less implant palpability and visibility. Conclusion. The encouraging results of these preliminary cases may open new horizons for a multidisciplinary approach in treating instrumentation-related CLBP. Fat grafting may represent a valid and minimal invasive option to be taken into account when established therapeutic options fail. Further experience with longer follow-up is needed to confirm our findings. Level of Evidence: 5 Patients undergoing successful spinal fusion surgery may experience instrumentation-related chronic low back pain (CLBP). Treatment options vary from conservative treatment till instrumentation removal, with no consensus on their efficacy. Fat grafting may drastically ameliorate instrumentationrelated CLBP, prospectively opening new horizons fort a potential multidisciplinary approach in treating this condition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".