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 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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.142 | 0.023 |
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".