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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".