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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".