Impact of Physiotherapy Intervention on Pain, Quality of Life, and Function in Low Back Pain Associated With Piriformis Syndrome: Protocol for Systematic Review
Bibliographic record
Abstract
Background: Piriformis syndrome is a neuromuscular condition with hip and buttock pain and other symptoms, including referred pain towards the lower back and leg and radiating towards the foot's medial aspect. Similarly, low back pain caused by piriformis syndrome is undetected or difficult to diagnose because of similar symptoms of lumbar disc herniation, lumbar stenosis, or radiculopathy, as well as neurogenic pain. A study conducted in 2013 found 2910 patients experienced low back pain with sciatica, which is the most common cause of low back pain, because of piriformis muscle stiffness. The prevalence of low back pain in piriformis syndrome is 5%-36%. It is more commonly seen in women than men. Objective: This systematic review protocol seeks to identify evidence whether physiotherapy interventions effectively relieve pain, improve functional outcomes, and enhance quality of life among individuals experiencing low back pain associated with piriformis syndrome. Methods: This review will analyze randomized controlled trials (RCTs) that include physiotherapy for patients with low back pain linked to piriformis syndrome. The included studies must report on pain levels or improvements in function related to quality-of-life outcomes. Searches will take place using Google Scholar, Pubmed/MEDLINE, the Cochrane Library, and PEDro for articles published from January 2014 to January 2025. Two reviewers will individually check the studies, choose relevant ones, and collect data while assessing quality using the Cochrane risk of bias tool. We will provide a narrative summary of the findings, concentrating on data about pain management, functional improvement, and quality of life enhancements. Results: This review will synthesize knowledge focusing on pain, quality of life and functions in low back pain, which is associated with piriformis syndrome. A synthesis of the findings will be conducted to determine which components of the interventions identified were the most advantageous to the patient population. Conclusions: The systematic review protocol is designed to identify the effectiveness of physiotherapy interventions for managing low back pain in piriformis syndrome. This analysis will review RCTs with evidence-based recommendations on reducing pain, improving function, and enhancing quality of life.
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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 | 0.055 | 0.074 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.024 | 0.026 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".