The Reduction of Cervical Hyperlordosis and Resolution of Craniocervical Symptoms in an Adolescent Female: A Chiropractic Biophysics Case Report With Long-Term Follow-Up
Bibliographic record
Abstract
(CBP®) technique protocols to reduce a hyperlordotic cervical spine corresponding with many craniocervical symptoms, including chronic migraines and neck pain. A 15-year-old female presented with chronic headaches, neck pain, and neck stiffness among other complaints following a martial arts sprain injury several months prior. There were many positive orthopedic tests and limited range of motion. Radiographs revealed a cervical hyperlordosis and a right lateral head translation. CBP® treatment was given and involved cervical distraction traction as well as corrective exercises twice a week for 12 weeks, and then monthly for one year with a complementary home program. After 12 weeks, there was a full recovery from migraines and neck pain correlating with an 8° reduction in lordosis and correction of head translation. At 15 months, the patient remained well and achieved a 13° total reduction in the neck curve. This is the first case documenting the successful application of CBP® methods to reduce cervical spine hyperlordosis in peer-reviewed literature. We propose too much curve may be as detrimental as too little curve in the cervical spine with respect to causing adverse stresses and strains in the surrounding soft tissues leading to pathological processes and nociceptive tendencies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".