Understanding how therapeutic exercise prescription changes outcomes important to patients with persistent non-specific low back pain: a realist review protocol
Bibliographic record
Abstract
INTRODUCTION: Persistent low back pain (LBP) is the leading cause of disability worldwide, and therapeutic exercise is recommended as a first-line treatment in international guidelines. The effects of exercise on clinical outcomes of pain and physical function are small to moderate, despite broader impacts on cardiovascular systems, biological health, mood, and emotional well-being. Therapeutic exercise prescription is defined as exercise that is prescribed by a clinician for a health-related treatment. It is unknown how therapeutic exercise prescription creates effects on outcomes of importance. Realist reviews explore how underlying mechanisms (M) may be active in the context (C) of certain situations, settings, or populations to create an intended or unintended outcome (O). Our objective is to explore and understand the mechanisms by which therapeutic exercise prescription changes outcomes for people with persistent LBP. METHODS: We will develop initial programme theories based on preliminary data from a previous systematic review and consensus workshop. These theories will be modified with input from a steering group (experts), a stakeholder group (people with lived experience of exercise for persistent LBP and clinicians), and a scoping search of the published literature. An information specialist will design and undertake an iterative search strategy. These will be used to create CMO configurations, which will be refined and tested using the literature. The realist review will be reported following RAMESES guidance. DISCUSSION: Realist reviews are uncommon in LBP research to date, yet those offer an opportunity to contrast with traditional methods of randomised controlled trials and systematic reviews and provide additional information regarding the contexts and mechanisms that may trigger certain outcomes. This can aid our understanding of the contextual features that may influence exercise prescription, such as for whom they are most effective, in what setting, how they are implemented and why. This realist synthesis will enhance our understanding of therapeutic exercise prescription to improve adherence and engagement and ultimately will provide clinically relevant recommendations regarding exercise prescription for those with persistent LBP. SYSTEMATIC REVIEW REGISTRATION: The review has been registered with PROSPERO (CRD42017072023).
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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 | 0.114 | 0.133 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.062 | 0.011 |
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".