Non-surgical Approaches in the Treatment of Lower Back Pain: A Review of Methods, Efficacy, and Safety
Bibliographic record
Abstract
Low back pain (LBP) is among the most common musculoskeletal disorders globally, representing a leading cause of disability and healthcare expenditure. Its prevalence is expected to rise significantly in the coming decades, emphasizing the need for effective, evidence-based treatment strategies. This narrative review explores non-surgical approaches to LBP management, focusing on pharmacological and non-pharmacological therapies. It discusses the classification, pathophysiology, and red flag symptoms, alongside an evaluation of conservative treatments including NSAIDs, acetaminophen, muscle relaxants, opioids, antidepressants, physical therapy, acupuncture, spinal manipulation, and psychosocial interventions. The findings indicate that NSAIDs offer modest short-term relief and remain first-line pharmacological agents, while acetaminophen has limited efficacy. Muscle relaxants may benefit acute cases but carry notable side effects, particularly in older adults. Opioids, though effective in the short term, show minimal long-term benefit and a high risk of dependence. Non-pharmacological treatments—especially exercise therapy, manual therapy, and cognitive-behavioral interventions—demonstrate consistent efficacy in reducing pain and improving function. In conclusion, optimal management of LBP necessitates an individualized, multimodal approach that integrates pharmacological options with physical and psychological strategies to minimize harm, enhance function, and address biopsychosocial contributors to 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".