Failed back surgery syndrome successfully ameliorated with Chiropractic Biophysics<sup>®</sup> structural rehabilitation improving pain, disability as well as sagittal and coronal balance: a Chiropractic Biophysics<sup>®</sup> case report with a 6 year follow-up
Bibliographic record
Abstract
[Purpose] To present the case of the amelioration of chronic pain and disability in a patient suffering from failed back surgery syndrome. [Participant and Methods] A 27-year-old male with chronic low back pain was treated with a Coflex® intra-spinous instrument, however, it was removed shortly after due to poor outcome including worsening pain and disability. Radiographic assessment revealed significant posterior translation of the thorax complicated by significant loss of the normal lumbar lordosis and a left lateral translated thoracic cage posture. Chiropractic Biophysics® technique was applied over a 5.5-month period leading to structural spine improvements as well as improved pain, Oswestry disability index (ODI) and quality of life (QOL). [Results] There was a 21 mm reduction in posterior thoracic translation, a 6.2° improvement in lumbar lordosis and a 16 mm reduction in lateral thoracic translation corresponding with improved ODI and QOL scores. A 6 year follow-up showed successful outcome despite some degenerative changes in the spine at the prior surgical level. [Conclusion] This case adds to the growing literature showing the efficacy of non-surgical spinal rehabilitative methods in improving outcomes in patients with spinal deformity and associated disabilities. This case also demonstrates necessity of the continued criterion standard of spinal radiography for biomechanical assessment.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".