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Record W2741874250 · doi:10.1177/2325967117s00430

Short-term Impact of Anterior Cruciate Ligament Reconstruction in an Adolescent Population on 3D Knee Kinematics

2017· article· en· W2741874250 on OpenAlexaff
Guillaume Laforest, Alexandre Fuentes, Marc Therrien, Guy Grimard

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
Fundersnot available
KeywordsMedicineHamstringAnterior cruciate ligamentSagittal planeValgusPhysical medicine and rehabilitationAnterior cruciate ligament reconstructionGaitPopulationPhysical therapyGait analysisKinematicsKnee JointSurgeryAnatomy

Abstract

fetched live from OpenAlex

Objectives: Gait analysis is a proven method for assessing knee biomechanical adaptations in anterior cruciate ligament deficient (ACLD) patients and to quantify the impact of the reconstructive surgery (ACLR). In an adult population, ACLR has shown partial kinematic correction, as they remain in internal tibial rotation, putting them at risk of rotational instability and develop osteoartitis. ACLD adolescents likely adopt similar gait changes to reduce knee instability, but may show quicker or more return to the normal compared to adults. The purpose of this study is to compare the tridimensional (3D) knee kinematics before and after ACLR in adolescents. Methods: 25 ACLD patients aged between 13 and 19 years old on a waiting list for ACLR were recruited. Bilateral knee 3D kinematic data was gathered during treadmill walking at comfortable self-selected speed using the KneeKG TM System before and 6-9 months after ACLR. Each participant underwent the same surgical protocol (2-strand hamstring tendon graft, hamstring harvest proximally and kept distally, transphyseal tibial tunnel and transepiphyseal femoral tunnel) by the same experienced surgeon. Three analyses were done: ACLD knee pre vs. post-surgery, contralateral healthy knee pre vs. post-surgery, and ACLR knee vs. healthy contralateral knee. Results: In the sagittal and transverse planes, kinematic patterns remained similar to the pre-op; maintaining the characteristic knee flexion gait pattern during single support phase which is well documented in adult ACLD (hamstring facilitation strategy). The ACLR knee exhibited a more valgus dynamic alignment compared to pre-surgery throughout the gait cycle. The healthy contralateral knee kinematics displayed no significant changes in the frontal and sagittal planes after surgery compared to the pre-op pattern. In the transverse plane, post-op gait analysis showed significantly more external rotation at heel-strike and through weight acceptance. While looking at the gait symmetry post-surgery, ACLR knee revealed significantly more valgus while remaining in greater flexion during single limb support. In the transverse plane, an external rotation tendency is observed in the ACLR knee throughout the gait cycle, except during weight acceptance. Conclusion: This study shows that kinematic adaptations found in ACLD adolescents are likely to endure short term after surgery. For example, the greater knee flexion gait pattern used before surgery is still used after ACLR. Interestingly, adolescents used an external tibial rotation adaptation to limit internal tibial instability before the surgery and this pattern was still present post-surgery. Even if theses protection strategies are efficient, the increased dynamical valgus movement seen could place the graft under pressure while increasing patellofemoral stresses, a common complaint after ACLR. Lastly, post-surgery, the contralateral knee seems to mimic the external tibial rotation gait pattern strategy seen in ACL-knee. This study demonstrates that gait protective strategies are still present short term after the surgery. Biomechanical assessment helps ID these adaptations to optimize post-op rehabilitation and prevent secondary injuries.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.342
Teacher spread0.320 · 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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Citations1
Published2017
Admission routes1
Has abstractyes

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