Investigating the Mechanisms of Graded Sensorimotor Precision Training in Adults With Chronic Nonspecific Low Back Pain: Protocol for a Causal Mediation Analysis of the RESOLVE Trial
Bibliographic record
Abstract
BACKGROUND: Chronic low back pain (CLBP) is a global health problem associated with an increasing burden on individuals, health care systems, and society. Common treatments for people with CLBP produce, on average, small short-term improvements in pain and function compared with minimal care. The RESOLVE trial randomly allocated 276 people with CLBP to a new complex treatment strategy, pain education integrated with graded sensorimotor precision training (RESOLVE), or a sham control. The RESOLVE treatment was developed within a theoretical framework to target possible treatment mechanisms associated with CLBP development and persistence. OBJECTIVE: This protocol describes the planned evaluation of these proposed treatment mechanisms. Improved understanding of the mechanisms underpinning the RESOLVE treatment may guide its refinement and implementation. METHODS: We will use causal mediation analysis to evaluate the proposed treatment mechanisms, including pain self-efficacy, back beliefs, pain catastrophizing, kinesiophobia, back perception, tactile acuity, and movement coordination. The primary outcomes are pain intensity and function at 18 weeks following allocation. Data were collected blind to allocation and hypotheses at baseline (mediators, outcomes, confounders), end of treatment (mediators), and at 18 weeks following allocation (outcomes). We will test the robustness of our findings by conducting planned sensitivity analyses. RESULTS: Ethical approval was granted by the University of New South Wales Human Research Ethics Committee (HC15357). A total of 276 participants have been recruited from primary care practices and the community in Sydney, Australia. CONCLUSIONS: The RESOLVE treatment constitutes a new paradigm for CLBP management with potentially wide-reaching implications. This mechanistic evaluation will provide evidence for the hypothesized treatment mechanisms and help explain why the treatment strategy did or did not have an effect on patient-reported outcomes. These results will help guide the treatment refinement and implementation. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12615000610538; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368619&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/26053.
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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 | 0.054 | 0.054 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.015 |
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".