Cervical extension traction as part of a multimodal rehabilitation program relieves whiplash-associated disorders in a patient having failed previous chiropractic treatment: a CBP<sup>®</sup> case report
Bibliographic record
Abstract
[Purpose] To present the case of the non-surgical restoration of cervical lordosis in a patient suffering from chronic whiplash syndrome including chronic neck pain and daily headaches resulting from previous whiplash. [Subject and Methods] A 31 year old female presented with a chief complaint of chronic neck pain and headaches for 12 years, correlating temporally with a sustained whiplash. These symptoms were not significantly relieved by previous chiropractic spinal manipulative therapy. The patient had cervical hypolordosis and was treated with Chiropractic BioPhysics® protocol including extension exercises, manual adjustments and cervical extension traction designed to increase the cervical lordosis. [Results] The patient received 30 treatments over approximately 5-months. Upon re-assessment, there was a significant increase in global C2–C7 lordosis, corresponding with the reduction in neck pain and headaches. [Conclusion] This case adds to the accumulating evidence that restoring lordosis may be key in treating chronic whiplash syndrome. We suggest that patients presenting with neck pain and/or headaches with cervical hypolordosis be treated with a program of care that involves cervical extension traction methods to restore the normal cervical lordosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 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".