Chiropractic High-Velocity Low-Amplitude Spinal Manipulation in the Treatment of a Case of Postsurgical Chronic Cauda Equina Syndrome
Bibliographic record
Abstract
OBJECTIVE: To present an evidence-based case report on the use of chiropractic high-velocity low-amplitude spinal manipulation in the treatment a postsurgical, chronic cauda equina syndrome patient. CLINICAL FEATURES: A 35-year-old woman presented with complaints of midback pain, low-back pain, buttock pain, saddle anesthesia, and bladder and bowel incontinence, all of 6 months duration. The patient was 6 months post emergency surgery for acute cauda equina syndrome due to lumbar disc herniation. She had been released from neurosurgical care with the current symptoms considered to be residual and nonprogressive. INTERVENTION AND OUTCOME: The patient was treated with high-velocity low-amplitude spinal manipulation and ancillary myofascial release. After 4 treatments, the patient reported full resolution of midback, low back, and buttock pain. The patient was seen another 4 times with no improvement in her neurologic symptoms. No adverse effects were noted. CONCLUSION: This appears to be the first published case of chiropractic high-velocity low-amplitude spinal manipulation being used for a patient with chronic cauda equina syndrome. It seems that this type of spinal manipulation was safe and effective for reducing back pain and had no effect on neurologic deficits in this case.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".