Improving posture to reduce the symptoms of Parkinson’s: a CBP<sup>®</sup> case report with a 21 month follow-up
Bibliographic record
Abstract
[Purpose] To demonstrate the reduction of symptoms related to Parkinson's disease by improvement in posture. [Participant and Methods] A 59-year-old male patient presented with a prior diagnosis of Parkinson's. Symptoms included a resting right hand tremor, intermittent 'freezing episodes' with gait, mild ataxia with shuffling on toes and bradykinesia assisted with a cane, as well as low back pain and right knee pain. Radiography revealed gross postural and spine deformity. The patient received Chiropractic BioPhysics care including mirror image exercises, spinal traction, spinal adjustments as well as gait rehabilitation. [Results] After 38 treatments over 5 months, the patient had significant improvements in posture alignment as well as gait, balance, hand tremors, low back and knee pains and SF-36 values. A 21 month follow-up revealed the patient had remained essentially well and the initial postural improvements were maintained. [Conclusion] This case demonstrates improvement of various symptoms in a patient with Parkinson's disease. Since poor posture is a long known clinical manifestation of this disorder, it is proposed that the improvement of posture in these patients may lead to improved outcomes. X-ray use in the diagnosis and management in those with spine deformity is safe and not carcinogenic.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".