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Record W2898805336 · doi:10.1177/0363546518803359

There Are No Kinematic Differences Between Inframeniscal and Suprameniscal Anterolateral Ligament Injury in the Anterior Cruciate Ligament–Deficient Knee

2018· article· en· W2898805336 on OpenAlexaff
Timothy A. Burkhart, Manoj Matthew, W. Scott McGuffin, Alexandra M. Blokker, David W. Holdsworth, Ryan M. Degen, Alan Getgood

Bibliographic record

VenueThe American Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsFowler Kennedy Sport Medicine ClinicLawson Health Research InstituteWestern University
Fundersnot available
KeywordsAnterolateral ligamentCadaveric spasmAnterior cruciate ligamentAnatomyKinematicsMeniscusInternal rotationLigamentValgusKnee JointOrthodonticsKnee flexionMedicineLateral meniscusAnterior cruciate ligament reconstructionSurgeryMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

BACKGROUND: Previous research demonstrated that the attachment of the anterolateral ligament (ALL) to the lateral meniscus is stiffer and stronger in its tibial attachment than its femoral attachment. How this relates to anterolateral knee stability and lateral meniscal function is unknown. HYPOTHESIS/PURPOSE: The hypothesis was that the ALL acts as a peripheral anchor to the lateral meniscus, aiding in anterolateral rotatory stability, and that the inframeniscal fibers of the ALL will provide greater anterolateral rotatory stability because of their greater tensile properties. The purpose was therefore to compare the difference in kinematics of the anterior cruciate ligament (ACL)-deficient knee between the infra- and suprameniscal ALL-sectioned states. STUDY DESIGN: Controlled laboratory study. METHODS: Eight paired fresh-frozen cadaveric knees were tested in a 5-degree of freedom loading jig under the following loading conditions: 5-N·m internal rotation at 15° incremental angles of flexion and combined 5-N·m internal rotation moment, 10-N·m valgus moment, and 88-N anterior translation force representing a pivot shift test at 0°, 15°, and 30° of flexion. The knees were tested under intact, ACL-deficient, and ACL-/ALL-deficient conditions, with the pairs of knees being randomized to either supra- or inframeniscal ALL sectioning. Resultant joint kinematics and tibiofemoral translations were measured and compared with a 2-way mixed repeated measures analysis of variance. RESULTS: Internal rotation increased by 3° after sectioning of the ACL at 0° of knee flexion ( P = .035). At 45° of knee flexion, internal rotation increased significantly by 2° between the ACL-deficient and the ACL-/ALL-deficient conditions ( P = .049). Secondary kinematics of valgus and anterior translation were observed in response to the 5-N·m load after ACL and ALL sectioning. Analysis of the pivot shift showed increases in tibiofemoral translation after sectioning of the ACL, with further translations after sectioning of the ALL. No differences were observed between supra- and inframeniscal ALL sectioning under any of the loading conditions. CONCLUSION: An injury to the ALL, coexisting with ACL deficiency, results in only minor increases in knee joint patholaxity. No differences in pivot-shift kinematics or tibiofemoral rotations were observed between the supra- and inframeniscal sectioning of the ALL in the ACL-deficient knee. CLINICAL RELEVANCE: Tears of the midbody and/or posterior root attachment of the lateral meniscus are often observed at the time of ACL reconstruction. Increased anterolateral rotatory laxity has been observed in both lateral meniscus- and ALL-deficient states in combination with an ACL injury. While no significant functional relationship was found between the ALL and lateral meniscus, ALL sectioning did result in increased knee joint patholaxity, as demonstrated by composite tibiofemoral rotations.

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: Observational · Consensus signal: none
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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.286
Teacher spread0.275 · 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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Citations4
Published2018
Admission routes1
Has abstractyes

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