EFFECTIVENESS OF CHIROPRACTIC ADJUSTMENT IN LUMBAR PAIN IN CROSSFIT PRACTITIONERS
Bibliographic record
Abstract
ABSTRACT Objective: To evaluate the efficacy of acute chiropractic adjustment in individuals who practice CrossFit with regard to complaints of low back pain and the joint range of motion in this region. Methods: A randomized clinical trial comprised of CrossFit practitioners from a box in Novo Hamburgo-RS, of both sexes and aged 18 to 40 years who had low back pain at the time of the study. The following tools were used: Semi-structured Anamnesis Questionnaire, Visual Analog Scale, McGill Pain Questionnaire, and SF-36 Quality of Life Questionnaire. Individuals in the control group answered the questionnaires before and after CrossFit training. The chiropractic group performed the same procedure, plus pre-training chiropractic adjustment and joint range of motion (ROM) before and after lumbar adjustment. Results: There was a significant increase in pain in the control group, and a significant decrease in pain in the chiropractic group, including one day after the chiropractic adjustment. In the chiropractic group, the joint ranges of motion had a significant increase in flexion and extension of the lumbar spine after chiropractic adjustment. Conclusion: The chiropractic group achieved a significant improvement in pain level and joint range of motion, suggesting that acute chiropractic adjustment was effective in reducing low back pain.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".