Spinal Intervention Efficacy on Correcting Cervical Vertebral Axes of Rotation and the Resulting Improvements in Pain, Disability and Psychsocial Measures
Bibliographic record
Abstract
Objectives: Mean axes of rotation [MAR] of cervical joints are an effective measure of spine pathology. Khan Kinetic Treatment [KKT] is known to relieve symptoms, but its biomechanical effects have not been quantified. This study assesses KKT efficacy using MAR correction and its associated effects. Methods: The intervention applies vibrations via stylus to a bony landmark of the spine. Using saggital plane cervical X-rays, pre-post intervention MARs were computed for 44 patients with chronic neck pain. The study was randomized, single blinded, and sham controlled for outcome measure comparisons. Mechanical input was assessed using a load cell and vertebral acceleration and the outcome measures were: 1. cervical MARs, 2. self-reported neck pain, 3. neck disability index scores, and 4. psycho-social assessments. Results: 1. Average peak force on vertebrae during treatment was 10.3 N and the average peak acceleration was 2.19G, 2. KKT improved pain and neck disability scores significantly over shams, 3. KKT corrected 62 percent of abnormal MARs with significantly larger MAR vector magnitude differences [pre-post] at the C5-6 level than shams, 4. in patients without changes in MAR locations, KKT significantly improved neck disability scores above shams, 5. MAR correction was significantly related to improving both pain and neck disability across all subjects. Conclusions: We present biomechanical evidence of spinal “re-alignment” and its ability to improve both pain and neck disability. Capacity to improve neck disability despite no change in MAR locations indicates that MAR correction, while effective, is not the sole mechanism behind the interventions success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".