STOPS approach to individualised physiotherapy versus usual physiotherapy care for chronic low back pain in India: A randomised controlled trial protocol
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is the leading cause of disability worldwide, particularly in low- and middle-income countries such as India. Many treatment approaches fail to address the multidimensional nature of LBP, leading to suboptimal outcomes. The Specific Treatment of Problems of the Spine (STOPS) approach addresses biological, neurophysiological, and psychosocial factors to deliver individualised physiotherapy for LBP, yet its effectiveness in India has not been explored. OBJECTIVE: This study aims to evaluate the effectiveness of individualised physiotherapy using the STOPS approach compared to usual physiotherapy care in individuals with chronic low back pain (CLBP) in India. METHODS: This is a parallel-group superiority randomised controlled trial with blinding of participants, outcome assessors, and the data analyst. A total of 154 participants in India with CLBP will be recruited and randomised to receive 11 sessions of either individualised physiotherapy via the STOPS approach or usual physiotherapy care. The primary outcome is activity limitation measured using the Oswestry Disability Index at 26 weeks. Functional MRI and qualitative interviews will assess brain functional changes and participant experiences, respectively. Data will be analysed using intention-to-treat principles. CONCLUSION: This study will provide insights into the effectiveness of the STOPS approach to delivering individualised physiotherapy for CLBP in India, in comparison to usual physiotherapy care. TRIAL REGISTRATION: This trial is prospectively registered with the Clinical Trials Registry of India: CTRI/2024/08/072259, https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MTAyNjY2&Enc=&userName=2024/08/072259.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.009 |
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".