The Effect of Chiropractor Adjustment for Reducing Imbalances in Leg Strength
Bibliographic record
Abstract
Imbalance of strength between legs is a predictor of future injury (Knapik et al., 1991). PURPOSE: To determine whether chiropractor adjustment could reduce the differences in strength between legs in individuals with a strength imbalance. METHODS: Forty-nine subjects (28 males, 21 females, age 49±22y) who had at least a 15% difference in isometric strength between left and right legs for hip flexion, extension, or abduction, or knee flexion were randomized to receive either a chiropractor adjustment involving the lumbar spine and iliac crest, or a “mock” placebo adjustment. Strength was assessed before and immediately after the treatment on an isokinetic (Biodex) dynamometer. Subjects and the individual assessing strength were blinded to the treatment. RESULTS: Across all strength tests the subjects who received chiropractor adjustment had a significant reduction in the relative strength difference between legs (27±16% difference before and 10±11% difference after treatment) compared to placebo (23±14% difference before and 19±26% difference after) [p<0.05]. The reduction in relative strength difference between legs in the subjects receiving chiropractor treatment was due to an improvement in strength of the weaker leg (from 97±41 to 112±39 N; p<0.01) whereas the placebo group had no change (92±44 to 97±46 N). The strength of the stronger leg was not affected by treatment (119±45 to 123±45 N for the chiropractor group, and 110±50 to 113±59 N for the placebo group). For individual strength tests, the chiropractor adjustment was most effective for reducing relative strength differences for knee flexion (p<0.05), and hip flexion (p=0.054) compared to placebo. CONCLUSION: Chiropractor adjustment is effective for reducing imbalances in strength between legs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".