Effectiveness of Patellar Bracing for Treatment of Patellofemoral Pain Syndrome
Bibliographic record
Abstract
OBJECTIVE: To determine the effectiveness of patellar bracing for treatment of patellofemoral pain syndrome (PFPS). DESIGN: Prospective, randomized, single-blinded clinical trial. SETTING: Subjects recruited from the general population of the city of Calgary. SUBJECTS: A total of 136 subjects (79 females and 57 males with a total of 197 affected knees) diagnosed with PFPS. INTERVENTION: Subjects were randomly assigned to 1 of 4 treatment groups: (1) home exercise program, (2) patellar bracing, (3) home exercise program with patellar bracing, and (4) home exercise program with knee sleeve. OUTCOME MEASURES: The outcome measurements were knee function (KF) and 10-cm visual analogue scale (VAS) pain ratings for 3 different situations: knee pain during sport activity, knee pain 1 hour after sport activity, and knee pain after sitting with knees bent for 30 minutes. The outcome measurements were assessed at baseline and at 3, 6, and 12 weeks. The investigators were blinded to the treatment group of each subject. Calculations were made for 95% confidence intervals for the change in KF and VAS pain ratings from baseline measurement to 12 weeks. RESULTS: There was no difference in the 95% confidence intervals in the change of KF and VAS pain ratings among the 4 treatment groups over 12 weeks. CONCLUSIONS: Symptoms of PFPS improved over time in terms of pain and knee function regardless of the treatment group. Patellar bracing did not improve the symptoms of PFPS more quickly when added to a home program of leg strengthening. However, patellar bracing alone can improve the symptoms of PFPS.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".