Innovative application of Cox Flexion Distraction Decompression to the knee: a retrospective case series.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to introduce the application of Cox flexion distraction decompression as an innovative approach to treating knee pain and osteoarthritis. METHODS: Six months of clinical files from one chiropractic practice were retrospectively screened for patients who had been treated for knee pain. Twenty-five patients met the criteria for inclusion. The treatment provided was Cox flexion distraction decompression. Pre-treatment and post-treatment visual analog pain scales (VAS) were used to measure the results. In total, eight patients presented with acute knee pain (less than three months' duration) and 18 patients presented with chronic knee pain (greater than three months) including two patients with continued knee pain after prosthetic replacement surgery. RESULTS: For all 25 patients, a change was observed in the mean VAS scores from 7.7 to 1.8. The mean number of treatments was 5.3 over an average of 3.0 weeks. Acute patient mean VAS scores dropped from 8.1 to 1.1 within 4.8 treatments over 2.4 weeks. Chronic patient mean VAS scores dropped from 7.5 to 2.2 within 5.4 treatments over 3.3 weeks. No adverse events were reported. CONCLUSION: This study showed clinical improvement in patients with knee pain who were managed with Cox flexion distraction decompression applied to the knee.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".