Study protocol for a prospective, randomized controlled confirmatory clinical investigation to evaluate the safety and efficacy of a multidisciplinary digital therapeutics in patients with patellofemoral pain syndrome
Bibliographic record
Abstract
BACKGROUND: Patellofemoral pain is a prevalent knee condition affecting up to 40% of individuals, especially females aged teens to 50 s. Standard treatments, including exercise therapy, often yield insufficient long-term results, partly due to low compliance and psychological factors like depression and catastrophizing of pain. A digital therapeutics "MORA Cure PFP," which combines structured progressive exercise and cognitive behavioral therapy via an app, offers a solution to overcome the limitations of conventional treatment for patellofemoral pain patients. METHODS: To evaluate the safety and efficacy of MORA Cure PFP, a two-arm controlled trial will enroll 216 patients diagnosed with patellofemoral pain randomly assigned in a 1:1 ratio to treatment and control groups. The treatment group will use the app, while the control group will perform self-guided exercises using educational materials. This trial aims to determine if the treatment group shows greater reduction in usual pain intensity scores at 8 weeks compared to the control group. Additional assessments include worst pain, knee function, depression, and pain catastrophizing levels. DISCUSSION: Key design elements of the clinical trial, such as control group selection, inclusion/exclusion criteria, number of patients, and primary endpoint, were designed with consideration for not only medical perspectives but also regulatory aspects of software as a medical device, including device approval and health technology assessment. TRIAL REGISTRATION: ClinicalTrials.gov., NCT06260865, registered 15th February 2024.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".