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Record W4386194567 · doi:10.7759/cureus.44139

Using Dual-Orthogonal Fluoroscopy and CT to Assess the Relationship Between Knee Morphology and Patellar Kinematics in Patients With Patellofemoral Pain

2023· article· en· W4386194567 on OpenAlexaff
Joanna Yuen, Fateme Esfandiarpour, Constance Lebrun, Suki Dhillon

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Alberta HospitalUniversity of AlbertaMacEwan UniversityUniversity of British Columbia
Fundersnot available
KeywordsMedicineFluoroscopyPatellaKinematicsSupine positionOsteoarthritisKnee JointOrthodonticsPatellofemoral pain syndromeRadiologySurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Patellofemoral pain (PFP) is one of the most common knee overuse injuries, with studies suggesting PFP as a precursor for early knee osteoarthritis. The etiology of PFP is multi-factorial; however, patellar mal-tracking has been regarded as a primary mechanism. Details of this multi-factorial mechanism have been unclear because of the limitations in evaluating in-vivo, three-dimensional (3D) patellofemoral joint movement during dynamic activities accurately. Alternatively, studies have demonstrated the high accuracy and repeatability of dual fluoroscopy and CT/MRI for measuring knee joint motion. OBJECTIVE: This study uses dual fluoroscopy and CT to investigate the associations between joint morphology and patellar kinematics in healthy controls and subjects with PFP. METHODS: Eight PFP females (29.7±10.6 years) and 10 healthy females (25.0±7.7 years) were recruited and screened by a sports medicine physician. CT imaging was performed on participants in a supine with the knee extended, and ankle and hip in neutral alignment. Dual-orthogonal fluoroscopy measured patellar movement while participants performed a lunge task. A calibration algorithm was used to register the 3D CT model to 2D fluoroscopy image to calculate the relative position and angles of the patella based on the clinical definition of patellar motion. Measures of patellar and trochlear morphology were generated and correlated to kinematic data. RESULTS AND CONCLUSION: There was a significant difference in the patellar-to-trochlear width ratio; however, no other significant differences in CT morphology measurements were present between groups. For PFP patients in the weight-bearing extended position, there was a moderate positive correlation between the patellar-to-trochlear width ratio and medial-lateral patellar shift (τ = 0.643, p = 0.026). Healthy controls in this position demonstrated a moderate positive correlation between the lateral-trochlear inclination angle and medial-lateral patellar shift (τ = 0.600, p = 0.016) and moderate negative correlation between medial trochlear inclination angle and medial-lateral patellar shift (τ = -0.511, p = 0.040). The findings suggest that, for this cohort, there is correlation between morphology and patellar kinematics. Passive and active stabilizers likely have a role in mal-tracking.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.094
GPT teacher head0.284
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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