Cognitive behavioral therapy (CBT) and meditation in the treatment of persistent low back pain: Interventional Study
Bibliographic record
Abstract
Objective: To compare the effect of both CBT and Meditation in chronic lower back pain patients. Method: Participants fulfilling the exclusion and inclusion criteria and who are between the age group of 35-50 years with CLBP were included. Numerical pain rating scale and Montreal Cognitive Assessment (MOCA) was used for the participant selection. The participants were further divided into three groups and 4-week intervention of conventional physiotherapeutic exercise, meditation and CBT, was given to the participants. Numerical Pain Rating Scale and Oswestry Low Back Pain Disability Questionnaire were used as outcome measures. Results: 40 chronic back pain patients were enrolled and randomized. All enrolled participants completed baseline tests, providing cross-sectional data for this study. Simple randomization allocated 14 patients to the control group and 13 patients each to Experimental Group 1 and Experimental Group 2. Significant within-group improvements occurred on the Numerical Rating Scale and Oswestry scores between baseline and final visits for all groups. However, the experimental groups showed significantly greater decreases in pain intensity versus controls, evidenced by reduced mean Numerical Rating Scale and Oswestry scores at follow-up. One-way ANOVA and Welch tests revealed significantly reduced Numerical Rating Scale and Oswestry scores after treatment across groups. Both tests yielded statistically significant p-values <0.01. Conclusion: The findings show that meditation and cognitive behavioural therapy (CBT) are beneficial in reducing pain. As a result, for patients with persistent low back pain, taking into consideration these two treatment techniques is critical. Keywords: Cognitive Behavioural Therapy, Chronic Pain, Oswestry Questionnaire
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".