Lumbar extension traction alleviates symptoms and facilitates healing of disc herniation/sequestration in 6-weeks, following failed treatment from three previous chiropractors: a CBP<sup>®</sup> case report with an 8 year follow-up
Bibliographic record
Abstract
) protocol designed to improve the lumbar lordosis. [Subject and Methods] A 56-year-old male suffered from chronic low back pain and recent sciatica due to lumbar disc herniation despite being under continuous care from three previous chiropractors. Radiographic analysis revealed a lumbar hypolordosis and MRI confirmed disc herniation and sequestration at L4-L5. Generalized decreased lumbar range of motion and multiple positive orthopedic and neurologic tests were present. [Results] After 26 treatments of CBP lumbar extension traction over 9-weeks a total reduction of the disc herniation and sequestration occurred with concomitant improvement in neurologic symptoms. Continuing maintenance treatments, an 8 year follow-up shows no relapse of condition and patient remained in good health. [Conclusion] A patient with lumbar disc herniation/sequestration was successfully treated with CBP technique procedures including lumbar extension traction that achieved a significant healing of herniation and significant reduction in symptoms not obtained following traditional chiropractic procedures alone. The quick reduction in lumbar disc herniation would appear to be related to a segmental disc unloading force produced during extension traction procedures for increasing the lumbar curvature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".