Cognitive functional therapy versus therapeutic exercises for the treatment of individuals with chronic shoulder pain: A protocol for a randomized controlled trial
Bibliographic record
Abstract
INTRODUCTION: Shoulder pain is a debilitating musculoskeletal condition with functional, physical, and psychological impacts. Interventions for chronic shoulder pain should address the biopsychosocial model, with Cognitive Functional Therapy (CFT) emerging as a promising physiotherapy approach. CFT approaches the multidimensional nature of pain, integrating physical and cognitive aspects. To date, no study has assessed the effectiveness of CFT in individuals with chronic shoulder pain. Therefore, this randomized controlled trial aims to compare the effects of CFT to therapeutic exercises on pain intensity, disability, self-efficacy, sleep quality, biopsychosocial aspects, and central pain processing in individuals with chronic shoulder pain. METHODS: This will be a randomized controlled trial, single-blinded with two parallel groups. Seventy-two individuals with chronic shoulder pain will be randomly assigned to one of two groups: CFT or Therapeutic exercise. The interventions will last 8 weeks, with the CFT group receiving therapy once a week and the therapeutic exercise group receiving sessions twice a week. The primary outcomes will be pain intensity and disability, while the secondary outcomes will include function, self-efficacy, sleep quality, biopsychosocial factors, perception of improvement/deterioration, and central pain processing. The outcome measures will be assessed at baseline, 4th week, end of treatment (8th week), and 12th-week follow-up. CONCLUSION: The results of this study will contribute to understanding the effectiveness of CFT in treating individuals with chronic shoulder pain. Trial registration number: NCT06542666.
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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 | 0.040 | 0.037 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".