The effect of patellar taping on lower extremity running kinematics in individuals with patellofemoral pain syndrome
Bibliographic record
Abstract
Purpose: To investigate the effects of patellar taping (Leukotape® (LT), Pinetown, South Africa, Kinesio Tape (KT), Dortmund, Germany, or no tape) on lower extremity kinematics in runners with and without patellofemoral pain syndrome (PFPS). Methods: In total, 20 healthy individuals and 12 with PFPS ran on a treadmill under different taping conditions and lower extremity kinematics and stride characteristics were obtained using Peak Motus Software, Colorado, USA. Data were analyzed using descriptive statistics and mixed factorial analysis of variance (p < 0.05). Results: Significant taping effects were found for hip (F(2,60) = 16.79, p = 0.0001) and knee (F(2,60) = 17.27, p = 0.0001) flexion angles at initial contact, and peak hip flexion angles during swing (F(2,60) = 6.55, p = 0.003). Increased flexion was noted with LT more than KT and no tape conditions. Similarly, peak knee flexion angles during stance (F(2,60) = 3.51, p = 0.03) and flight time (F(2,60) = 5.01, p = 0.01) revealed significant taping effects, with LT resulting in more flexion (p = 0.04) and shorter flight times (p = 0.01) than the no tape condition. Furthermore, a significant taping effect was seen for peak knee flexion angle during swing (F(2,60) = 4.96, p = 0.01), with the KT resulting in less flexion than LT (p = 0.04) and no tape conditions (p = 0.04). Conclusion: The application of tape during running may impact on hip and knee flexion angles at initial contact, as well as flight time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".