Optimising management of low back pain through the pain and disability drivers management model: Findings from a pilot cluster nonrandomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Low back pain (LBP) remains the leading cause of disability. The Low Back Pain and Disability Drivers Management (PDDM) model aims to identify the domains driving pain and disability to guide clinical decisions. The objectives of this study were to determine the feasibility of conducting a pragmatic controlled trial of the PDDM model and to explore its effectiveness compared to clinical practice guidelines' recommendations for LBP management. METHODS: A pilot cluster nonrandomised controlled trial. Participants included physiotherapists and their patients aged 18 years or older presenting with a primary complaint of LBP. Primary outcomes were the feasibility of the trial design. Secondary exploratory analyses were conducted on LBP-related outcomes such as pain severity and interference at 12-week follow-up. RESULTS: Feasibility of study procedures were confirmed, recruitment exceeded our target number of participants, and the eligibility criteria were deemed suitable. Lost to follow-up at 12 weeks was higher than expected (43.0%) and physiotherapists' compliance rates to the study protocol was lower than our predefined threshold (75.0% vs. 57.5%). A total of 44 physiotherapists and 91 patients were recruited. Recommendations for a larger scale trial were formulated. The PDDM model group demonstrated slightly better improvements in all clinical outcome measures compared to the control group at 12 weeks. CONCLUSION: The findings support the feasibility of conducting such trial contingent upon a few recommendations to foster proper future planning to determine the effectiveness of the PDDM model. Our results provide preliminary evidence of the PDDM model effectiveness to optimise LBP management. CLINICAL TRIAL REGISTRATION: Clinicaltrial.gov, NCT04893369.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".