Effect of patellofemoral joint targeted education, and exercise guidelines on outcomes of patients with patellofemoral osteoarthritis
Bibliographic record
Abstract
Background and objective: Patellofemoral joint osteoarthritis (PFJOA) is an under recognized category of arthritis, evident in almost 70% of adults with knee pain. Objective was to evaluate the effect of patellofemoral joint targeted education, and exercise guidelines on outcomes of patients with patellofemoral osteoarthritis.Methods: A quasi experimental (pre/post) design was used. Setting: The study was conducted in the physiotherapy department of a large University Hospital in Egypt. Sample: A randomized 30 adult patients with symptomatic and diagnosed PFJOA. Researchers and Physiotherapists delivered the PFJ-targeted education, and exercise program in 3 sessions over 9 month period.Results: The PFJ-targeted education, and exercise guidelines resulted in a highly statistically significant difference in the Knee Injury and Osteoarthritis Outcome Score (KOOS) pre/posttest in the whole five domains of the questionnaire; Pain (nine items); Symptoms (seven items); ADL Function (17 items); Sport and Recreation Function (five items); and Quality of Life (four items) p < .001**.Conclusions: PFJ-targeted education, and exercise guidelines were more effective in reducing pain, improving physical function, and activities of daily living. Recommendation: Replication of the study using a larger probability sample from different geographical areas to help for generalization of the results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".