Efficacy and acceptability of pharmacological and non-pharmacological interventions for non-specific chronic low back pain: a protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite the enormous financial and humanistic burden of chronic low back pain (CLBP), there is little consensus on what constitutes the best treatment options from a multitude of competing interventions. The objective of this network meta-analysis (NMA) is to determine the relative efficacy and acceptability of primary care treatments for non-specific CLBP, with the overarching aim of providing a comprehensive evidence base for informing treatment decisions. METHODS: We will perform a systematic search to identify randomised controlled trials of interventions endorsed in primary care guidelines for the treatment of non-specific CLBP in adults. Information sources searched will include major bibliographic databases (MEDLINE, Embase, CENTRAL, CINAHL, PsycINFO and LILACS) and clinical trial registries. Our primary outcomes will be patient-reported pain ratings and treatment acceptability (all-cause discontinuation), and secondary outcomes will be functional ability, quality of life and patient/physician ratings of overall improvement. A hierarchical Bayesian class-based NMA will be performed to determine the relative effects of different classes of pharmacological (NSAIDs, opioids, paracetamol, anti-depressants, muscle relaxants) and non-pharmacological (exercise, patient education, manual therapies, psychological therapy, multidisciplinary approaches, massage, acupuncture, mindfulness) interventions and individual treatments within a class (e.g. NSAIDs: diclofenac, ibuprofen, naproxen). We will conduct risk of bias assessments and threshold analysis to assess the robustness of the findings to potential bias. We will compute the effect of different interventions relative to placebo/no treatment for both short- and long-term efficacy and acceptability. DISCUSSION: While many factors are important in selecting an appropriate intervention for an individual patient, evidence for the analgesic effects and acceptability of a treatment are key factors in guiding this selection. Thus, this NMA will provide an important source of evidence to inform treatment decisions and future clinical guidelines. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registry number: CRD42019138115.
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 | 0.095 | 0.128 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.049 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".