Effect of Progressive Muscular Relaxation on Stress and Disability in Subjects with Chronic Low Back Pain
Bibliographic record
Abstract
Introduction:Back pain is the major cause of activity limitation in people younger than 45 years.70-85% of population suffer from back pain at some time in life. Approximately 5–10% of patients with low back pain develop chronic low back pain.ithasbecomea diagnosis of convenience for many perople who are actually disabled for work-related, orpsychologicalreasons.Chronic low back pain has a high prevalence in many countries around the worldand it affects 10% of adult population. Purpose of the study:To see the effect of progressive muscular relaxation on stress and disability associated with chronic low back pain. Methodology:30 subjects were included in the study with age group 18-30 years. All the subjects were random selected according to inclusion and exclusion criteria. All the subjects were randomly assigned into two groups,oneexperimental and other was control group. Subjects of experimental group were received progressive muscular relaxation, hot pack and only hot pack in control group. Outcome measures were noted as stress using DASS, Disability using Quebec pain disability scale and pain using VAS. All the measurements were done before the treatment session and after 1 st week,2 nd week ,3 rd week and 4 th week Result:Progressive muscular relaxation showed significant differences then control group for VAS, Stress and disability. Conclusion:It was concluded that Use of Progressive muscular relaxation as a treatment associated with a reduction in perception of pain, stress and improvement in wellbeing for the chronic low back pain patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 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".