Relief from Back Pain Through Postural Adjustment: a Controlled Clinical Trial of the Immediate Effects of Muscular Chains Therapy (MCT)
Bibliographic record
Abstract
INTRODUCTION: Back pain can be one of the most common health problems, causing suffering, disabilities, and financial losses. Postural models for pain treatment state that poor posture alters the joint position and causes pain, such as back pain. Muscular Chain Therapy (MCT) is a technique that is used to treat posture pathologies, among others. PURPOSE: The aim of the present study was to assess the efficiency of a single session of Muscular Chain Therapy (MCT) on complaints of undiagnosed musculoskeletal spinal pain. SETTING: Physical therapy clinic of the University of Center-West (Guarapuava, Brazil). PARTICIPANTS: 100 subjects, aged between 20 and 39 years, with complaints of spinal musculoskeletal pain. RESEARCH DESIGN: Randomized controlled trial. INTERVENTION: The participants were randomly assigned by a non-care provider into two groups: The MCT Group that received Muscular Chain Treatment and the Control Group that received a placebo treatment of 15 minutes turned off ultrasound therapy. All volunteers were assessed before and after treatment using an analog pain scale. A score of 0 indicated no pain and 10 was the maximum degree of pain on the scale. MAIN OUTCOME MEASURE: Degree of pain measured by analog scale. RESULTS: The chi-square goodness of fit test was used to compare gender distribution among groups displayed a p value = .25. Subject age had differences analyzed using the unpaired t test (p = .44). Pain assessment for treatment and placebo control groups was analyzed using a paired t test and unpaired t test. The paired t test was used for intragroup before/after treatment comparison (MCT p = .00001; Control Group p = .0001). The unpaired t test was used for comparing the difference of the pain level before and after treatment between groups (p = .0001). A priori statistical significance was set a p = .05. CONCLUSION: It is possible to conclude that one MCT session is an effective treatment of undiagnosed spinal musculoskeletal 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".