Increasing the cervical and lumbar lordosis is possible despite overt osteoarthritis and spinal stenosis using extension traction to relieve low back and leg pain in a 66-year-old surgical candidate: a CBP<sup>®</sup> case report
Bibliographic record
Abstract
[Purpose] To present the case of the dramatic relief of low back pain, leg pain and disability in an older female with osteoarthritis, lumbar spinal stenosis and hypolordosis of the cervical and lumbar spine. [Participant and Methods] A 66-year-old female presented with chronic low back pain, right leg pain, numbness and weakness. Despite being recommended for surgery, the patient sought alternative treatment. The patient was treated with Chiropractic BioPhysics® rehabilitation of the spine with the objective to increase the lumbar and cervical lordoses. Cervical and lumbar extension exercises and traction were performed as well as spinal manipulation. Treatment was performed approximately three times per week for 6.5 months. [Results] Re-assessment after treatment demonstrated significant reduction of low back pain, leg pain and other health improvements. X-rays showed structural improvements in the cervical and lumbar spine despite advanced osteoarthritis. [Conclusion] Lumbar and cervical hypolordosis subluxation may be increased in those with spinal deformity caused symptoms, despite the presence of osteoarthritis and degenerative stenosis of the spine. Spinal x-rays as used in the assessment and monitoring of patients being treated with contemporary spinal rehabilitation methods are not harmful and should be used for routine screening purposes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".