Comparative effectiveness of manual therapy, pharmacological treatment, exercise therapy, and education for neck pain (COMPETE study): protocol of a systematic review with network meta-analysis
Bibliographic record
Abstract
BACKGROUND AND CONTEXT OF THE STUDY: Neck pain is a prevalent and globally burdensome problem. Clinical practice guidelines have recommended conservative treatments such as education, exercise therapy (ET), manual therapy (MT), and pharmacological therapy (i.e., medication) to manage all types of neck pain based on the chronicity of the disease (acute, subacute, and chronic pain). However, there is scarce evidence to determine which interventions constitute the most effective strategy for this condition. RESEARCH QUESTION: What are the best conservative treatment options (i.e., ET, MT, education, and/or medication) to relieve pain and disability-related outcomes in patients with neck pain? THE OVERALL PURPOSE OF THE STUDY: (1) To identify which type of conservative treatment (education, ET, MT, and/or medication) and their combinations have the greatest probability of being most effective for neck pain using a network meta-analysis (NMA) approach. (2) To rank these conservative treatments in terms of safety (when possible) and effectiveness for managing neck pain. METHODOLOGY: Systematic review (SR) with NMA of randomized controlled trials (RCTs). Studies should include adults (aged > 18) with neck pain who received any of the interventions of interest (education, ET, MT, and medication). The main outcome will be pain intensity. Searches will be conducted in Ovid Medline All®, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Scopus, and Cochrane Library Trials database. No language or publication date restrictions will be applied. The revised Cochrane Risk-of-Bias (RoB) tool for RCTs (RoB-2) will be used to evaluate RoB, and the certainty of evidence will be evaluated by Grading of Recommendations, Assessment, Development, and Evaluations (GRADE). NMAs will be conducted to rank interventions according to their effectiveness and safety (when possible), allowing a comprehensive analysis of all available evidence, with different nodes specified for all conservative interventions of interest, placebo, sham therapy, and non-intervention control. This NMA will help clinicians and the scientific community choose the most effective strategy or combinations of strategies for treating neck pain. The information gathered in this project will inform decision-making and guide personalized care of individual patients in the future.
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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 | 0.063 | 0.084 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 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".