Physiotherapy versus pain medication for managing chronic cervical radiculopathy: protocol for a multi-arm parallel-group randomized clinical trial
Bibliographic record
Abstract
Background: Cervical radiculopathy (CR) is one of the prevalent causes of neck pain and disability. Physiotherapy and pain medications are the common nonoperative management, and in physiotherapy, there are many concepts of assessment and management. This study aims to determine the comparative effectiveness of three specialized physiotherapy approaches or only pain medications for managing CR cases. Methods: A prospective, assessor, and participant-blind, four-arm randomized control trial (RCT) has been planned to conduct on 160 patients with chronic cervical radiculopathy in 4 specialized centers of Dhaka city recruited between July and December 2022. Four groups (n=40) will be treated through structural diagnosis and management concept (SDM), regional approaches (RA), McKenzie mechanical diagnosis and therapy (MDT) concept prescribed by advanced practice physiotherapist (APP), or pain medications prescribed by the specialist physician for 4 weeks. The outcome will be evaluated in baseline, intermediate test (14 days), and post-treatment (28 days) through Brief Pain Inventory (BPI) for pain, Goniometer reading for cervical range of motion (ROM), and Neck disability index (NDI) as the primary outcome. The secondary outcome will be quality of life measured at baseline and post-treatment by the WHO quality of life questionnaire WHOQOL-BREF. Discussion: The study will compare the efficacy of the three physiotherapy approaches with pain medications when treating chronic cervical radiculopathy. The findings will provide evidence when demining the best conservative management approach for CR. Clinical Trial Registry India: CTRI/2022/03/040922 (08/03/2022)
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.069 | 0.012 |
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".