Pharmacotherapy Versus Exercise for Management of Low Back Pain: a Network Meta-analysis
Bibliographic record
Abstract
Abstract Background/aims: Use of pharmacological agents for low back pain (LBP) is more popular than physical exercises due to convenience, and administrative easiness. However, it is unclear whether pharmacotherapy is superior to exercises. The study aimed to examine efficacy of pharmacotherapy versus physical exercises for LBP. Materials/Methods: This is a network meta-analysis of randomized and non-randomised trials. We searched MEDLINE, PubMed, CINAHL, Academic Search Complete, and PsycINFO for articles published in English on use of pharmacotherapy and/or exercise in LBP management. Initial title, abstract screening and extraction were done following a predefined eligibility criteria. We used random-effect model of meta-analysis to estimate efficacy of pharmacotherapy and exercise, and network meta-analysis to compare their separate efficacies. We appraised quality of the included studies with aid of Cochrane Risk of Bias 1 and 2. Results: Relative to placebo, there were significant reductions in pain intensity with both pharmacotherapy (SMD = -0.769, 95% CI = -1.290-0.248, I2 = 96.634) and exercises (SMD = -1.563, CI = -2.784-0.342, I2 = 93.701). Direct comparison of pharmacotherapy and exercise showed insignificant reduction in pain intensity amongst individuals who received exercise compared to pharmacotherapy and exercise (SMD= -0.138, CI = -0.384 – 0.660). Indirect comparison showed no significant difference between pharmacotherapy and acupuncture (SMD = 0.023 (CI = -0.688 to 0.733). Overall, in favour of exercise, we obtained a combined estimate of SMD = -0.483 = (CI =-2.059 to 1.093). Conclusions: Exercise appears superior to pharmacotherapy for LBP, however exercise may not always be a preferred option.
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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.058 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".