Kinematic and Neuromuscular Deficiencies Phenotypes Associated With Patellofemoral Pain Syndrome : a Cross-sectional Interventional Study Protocol
Bibliographic record
Abstract
Background: The physiopathology of PFPS is multifactorial and includes static and dynamic dysfunctions which have not been fully elucidated to date. Among numerous classifications, a pragmatic classification distinguishes 3 major clinical phenotypes: PFPS with objective displacement of the patella, PFPS with extra-patellar alignment problem and PFPS without alignment problems. The relationships between the clinical and biomechanical factors are still unclear. Objective : The primary aim of this study is to describe and compare the kinematic deficiencies specifically associated with each of the 3 main clinical phenotypes. The secondary aim is to describe and compare neuromuscular deficiencies, postural and proprioceptive associated with each of the 3 main clinical phenotypes. Method: PHENOPAT is a comparative, non-randomized study. We will use the KneeKG device (EMOVI) to assess the 3D knee rotations, EoS Imaging to assess femoro-tibial alignment and isokinetic device to measure hip abductor, quadriceps and hamstrings muscle strength and endurance. The unipodal static and dynamic stability will be assessed by the Y test and posturography. We will compare the kinematic deficiencies using a rank comparison or a mean comparison test between 2 groups or between 3 groups according to the distribution of participants between the groups. Discussion: We expect to observe specific biomechanical factors correlated with each main clinical phenotypes. We wish that will help to enable a more specific diagnosis and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".