Autologous BMAC Therapy Improves Spinal Degenerative Joint Disease in Lower Back Pain Patients
Bibliographic record
Abstract
Spinal degenerative joint disease (DJD) is associated with lower back pain (LBP) arising from the degeneration of intervertebral discs (IVD), facet joints, intertransversarii muscles, and interspinous ligaments among other anatomical structures. To circumvent the socioeconomic burdens and often-problematic surgical options imposed by DJD therapy, cell-based biologic modalities like bone marrow aspirate concentrate (BMAC) have been investigated in pre-clinical and clinical settings, mostly for IVD degeneration (IDD), with encouraging outcomes. In this study, we evaluated the differences in therapeutic benefits of BMAC between IVD- and facet joint-originating chronic LBP. Eighteen patients diagnosed with chronic LBP met the selection criteria. Following discography and provocation testing, 13 patients tested positive and were assigned into IDD-associated LBP (1st arm), while the remaining 5 tested negative and were assigned into facetogenic LBP (2nd arm). Autologous BMAC was injected intradiscally in the 1st arm, while the 2nd arm received posterior spinal chain injections. No procedure-related serious events ensued. Clinical improvement was evaluated over 12 months based on pain and functionality questionnaires (VAS, BPI, RAND-36), opioid use, and changes in disc parameters assessed by magnetic resonance imaging (MRI). Ameliorated VAS and BPI scores differed significantly between both arms in favor of IDD patients who also took significantly less opioids. Average RAND-36 scores showed no significant difference between groups albeit a trend suggesting improvement was observed in IDD patients. MRI scans conducted on IDD patients demonstrated marked elevation in disc height and spinal canal space size without worsening disc quality. Overall, this is the first study investigating the potency of BMAC as an IDD treatment in Canada and the first globally for addressing facetogenic pain using cellular therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".