Improvement of Chronic Neck Pain After Posterior Atlantoaxial Surgical Fusion via Multimodal Chiropractic Care: A Case Report
Bibliographic record
Abstract
There is a lack of research regarding the effectiveness and safety of manual therapies, including spinal manipulative therapy (SMT), for patients with previous cervical spine surgery. A 66-year-old, otherwise healthy, woman who underwent C1/2 posterior surgical fusion for rotatory instability during adolescence presented to a chiropractor with a six-month history of progressive worsening of chronic neck pain and headaches despite acetaminophen, tramadol, and physical therapy. Upon examination, the chiropractor noted postural changes, limited cervical range of motion, and muscle hypertonicity. Computed tomography revealed a successful C1/2 fusion, and degenerative findings at C0/1, C2/3, C3/4, and C5/6, without cord compression. As the patient had no neurologic deficits or myelopathy and tolerated spinal mobilization well, the chiropractor applied cervical SMT, along with soft tissue manipulation, ultrasound therapy, mechanical traction, and thoracic SMT. The patient's pain was reduced to a mild level and the range of motion improved over three weeks of treatment. Benefits were maintained over a three-month follow-up as treatments were spaced apart. Despite the apparent success in the current case, evidence for manual therapies and SMT in patients with cervical spine surgery remains limited, and these therapies should be used with caution on an individual patient basis. Further research is needed to examine the safety of manual therapies and SMT in patients following cervical spine surgery and determine predictors of treatment response.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".