Chiropractic Management of the Kinetic Chain for the Treatment of Hip Osteoarthritis: An Australian Case Series
Bibliographic record
Abstract
OBJECTIVE: Osteoarthritis is the most common musculoskeletal disorder, estimated to affect 3 million Australians. Previous studies support structured exercise programs and manipulation for hip osteoarthritis; however, no trials have examined treatment of the lower limb kinetic chain. The purpose of this case series was to report hip range of motion and pain scale outcomes in 4 patients diagnosed with hip osteoarthritis who were treated with chiropractic management of the lower limb kinetic chain. METHODS: Four subjects (mean age 59.5; SD +/- 6.7) were provided with 9 sessions of chiropractic treatment. This included long-axis traction pulls and pre/post adjustment stretching of the symptomatic hip, with additional manipulation and mobilization of the lumbar spine, sacroiliac, knee, and ankle joints. Outcome measures included range of motion as measured and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: All 4 subjects had improvements in WOMAC scores, with a mean group reduction of 382.5 (SD +/- 115.8) and overall improvement of 68.1%. As a group, there were improvements in internal rotation (51.7%, mean 7.3 degrees; SD +/- 6.2 degrees), adduction (26.7%, mean 5.3 degrees; SD +/- 5.0 degrees), abduction (21.1%, mean 6.8 degrees; SD +/- 5.4 degrees), flexion (15.3%, mean 15 degrees; SD +/- 4.8 degrees) and external rotation (8.5%, mean 8.5 degrees; SD +/- 6.0 degrees). CONCLUSIONS: Four patients diagnosed with hip osteoarthritis had decreases in WOMAC scores and increases in hip range of motion after chiropractic management. Further research in the form of large scale randomized controlled trials is needed to investigate the effectiveness and clinical significance of chiropractic management for hip osteoarthritis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".