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Record W4399221082 · doi:10.1177/03635465241251462

Load Sharing of the Deep and Superficial Medial Collateral Ligaments, the Effect of a Partial Superficial Medial Collateral Injury, and Implications on ACL Load

2024· article· en· W4399221082 on OpenAlexaff
Wouter Beel, C. Doughty, Thiago Vivacqua, Alan Getgood, Ryan Willing

Bibliographic record

VenueThe American Journal of Sports Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsFowler Kennedy Sport Medicine ClinicWestern University
FundersAmerican Orthopaedic Society for Sports Medicine
KeywordsCadaveric spasmAnterior cruciate ligamentMedial collateral ligamentValgusACL injuryKinematicsMedicineKnee JointAnatomyLigamentSalidrosideRotation (mathematics)OrthodonticsSurgeryMathematicsPhysicsAnesthesiaGeometry

Abstract

fetched live from OpenAlex

BACKGROUND: Injuries to the deep medial collateral ligament (dMCL) and partial superficial MCL (psMCL) can cause anteromedial rotatory instability; however, the contribution of each these injuries in restraining anteromedial rotatory instability and the effect on the anterior cruciate ligament (ACL) load remain unknown. PURPOSE: To investigate the contributions of the different MCL structures in restraining tibiofemoral motion and to evaluate the load through the ACL after MCL injury, especially after combined dMCL/psMCL injury. STUDY DESIGN: Controlled laboratory study. METHODS: Sixteen fresh-frozen human cadaveric knees were tested using a 6 degrees of freedom robotic simulator. Tibiofemoral kinematic parameters were recorded at 0°, 30°, 60°, and 90° of knee flexion for the following measurements: 8-N·m valgus rotation, 4-N·m external tibial rotation (ER), 4-N·m internal tibial rotation, and a combined 89-N anterior tibial translation and 4-N·m ER for both anteromedial rotation (AMR) and anteromedial translation (AMT). The kinematic parameters of the 3 different MCL injuries (dMCL; dMCL/psMCL; dMCL/superficial MCL (sMCL)) were recorded and reapplied either in an ACL-deficient joint (load sharing) or before and after cutting the ACL (ACL load). The loads were calculated by applying the principle of superposition. RESULTS: < .05). In a comparison of the load through the ACL when the MCL was intact, the ACL load increased by 46% and 127% after dMCL injury and combined dMCL/psMCL injury, respectively, at 30° of knee flexion during ER. In valgus rotation, a significant increase in ACL load was seen only at 90° of knee flexion. CONCLUSION: The psMCL injury made the largest contribution to the reduction of net force/torque during AMR/AMT at 30° to 90° of flexion. Concomitant dMCL/psMCL injury increased the ACL load, mainly during ER. CLINICAL RELEVANCE: If a surgical procedure is being considered to treat anteromedial rotatory instability, then the procedure should focus on restoring sMCL function, as injury to this structure causes a major loss of the knee joint's capacity to restrain AMR/AMT.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.006
GPT teacher head0.280
Teacher spread0.274 · 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 designBench or experimental
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

Citations22
Published2024
Admission routes1
Has abstractyes

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