MétaCan
Menu
← Back to cohort
Record W2993756804

ALTERED DYNAMIC TIBIOFEMORAL CONTACT PATH LENGTH IN ACL DEFICIENT KNEES

2014· article· en· W2993756804 on OpenAlexvenueno aff
Chris Bhatla, Jillian E. Beveridge, Gregor Kuntze, Jena Shank, Gulshan B. Sharma, Payam Zandiyeh, Janet L. Ronsky

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAnterior cruciate ligamentMedicineOsteoarthritisTibiaFluoroscopyFemurKnee JointMagnetic resonance imagingAnatomyOrthodonticsSurgeryRadiologyPathology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Osteoarthritis (OA) is a degenerative joint disease characterised by the irreversible degradation of cartilage. Ligament injuries in the knee are a known risk factor for post-traumatic OA (PTOA), 1 the aetiology of which may be due to a combination of altered mechanical and biological factors. In this study, we investigated how anterior cruciate ligament (ACL) tear affects the relative motion of the subchondral bone surfaces in the knee (i.e., “surface interactions”), which is abnormal in ACL-deficient animal models. 2 Tibiofemoral contact path was calculated based on the relative surface motions. We hypothesised that contact path length and shape are altered in ACL-deficient subjects. METHODS Two  ACL-deficient subjects and one healthy control subject  (male, ages 34-55) underwent magnetic resonance (MR) imaging scans (3T FIESTA sequence) of both knees, then performed walking trials on an instrumented treadmill. During the walking trials, ground reaction force data were collected and fluoroscopy images from two separate views of the knee were taken. Using Amira (VSG, Germany), 3D models of the tibia and femur were generated from segmented MR images, and the in vivo bone alignments were determined using AutoScoper (Brown University, RI). For each in vivo frame, tibiofemoral proximity was mapped in Matlab (version R2013a, Natick, MA). Weighted centroids were calculated for each of the four tibiofemoral surfaces, with closer proximities having a higher weighting. The weighting factor used was w = (15 mm – proximity) 3 , counting only proximities less than 15 mm. Contact path was defined as the path that the weighted centroid made across the frames analyzed. Differences in contact path between left and right knees were assessed qualitatively for each of the subjects. RESULTS For the healthy control subject, contact paths were similar between knees, and were in a straight line, primarily in the anterior-posterior direction. In the two ACL-deficient subjects, the unaffected limb contact path displayed a shape similar to the control limbs. In contrast, the paths in the affected knees were shorter, were not consistently in the same location, and underwent greater mediolateral excursions. DISCUSSION AND CONCLUSIONS The qualitative results from three subjects support the hypothesis that contact path location and direction may be altered in ACL-deficient individuals. The changes in contact path show similarities to past studies with animal models. 2 There exist limitations in the ability to measure surface interactions, as only a small portion of the gait cycle can be analyzed using the dual-fluoroscopy system. Despite this, the dual-fluoroscopy method of bone tracking is superior to traditional marker-based motion capture systems, as it is more accurate than marker-based methods. The results suggest that there may be a correlation between ACL status and contact path shape during level walking. Future studies will increase the number of subjects and explore means of comparing contact paths quantitatively. Identification of associations between contact path shape and severity of joint damage may provide new insight into the pathogenesis of osteoarthritis.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.371
Teacher spread0.338 · 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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of undergraduate research in Alberta→Same topicKnee injuries and reconstruction techniques→French-language works237,207→