Exploring Predictors of Brace-Wearing Adherence in Non-Surgical Treatment of Acute Knee Medial Collateral Ligament Injuries
Bibliographic record
Abstract
OBJECTIVES: (1) To estimate adherence to brace wearing for medial collateral ligament (MCL) injuries across 3 phases of conventional treatment and (2) to explore predictors of adherence for each phase. DESIGN: Exploratory cohort study. SETTING: Primary care center. PATIENTS: Fifty-nine patients aged 18 to 65 (27 men, 32 women) from a randomized clinical trial examined the effectiveness of 2 bracing techniques (0-90 degrees or 30-90 degrees) for acute isolated MCL or combined anterior cruciate ligament injuries. Patients were prescribed a 6-week bracing protocol and were followed for 12 weeks. INTERVENTIONS: Patients were prescribed constant brace wearing for 4 weeks (∼23 h/d), then daytime wear only (∼15 h/d) until brace discontinuation at 6 weeks. Rehabilitation exercises were prescribed from 2 weeks onward. Adherence to the protocol was assessed through daily self-reported logs. Clinical and patient-reported outcomes were collected throughout the randomized clinical trial (baseline, 2, 4, and 6 weeks). This study interpreted them as predictor variables of treatment adherence alongside patient and treatment characteristics. MAIN OUTCOME MEASURES: Adherence to each 2-week phase, interpreted dichotomously (adherer or nonadherer). Adherers were identified as those who wore their brace according to the protocol. RESULTS: Adherence and pain decreased, while overall knee ratings improved throughout the treatment. Pain, affected knee, and brace range-of-motion settings were significant predictors of adherence in the exploratory logistic regressions. CONCLUSIONS: Pain, affected knee, and brace range-of-motion settings were the primary predictors of brace wearing in the first 4 weeks of treatment. This study is the first to provide insight into MCL bracing adherence, potentially aiding clinicians in treatment management.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".