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Record W2916194007 · doi:10.21037/aoj.2019.02.01

Consideration of lateral augmentation in anatomic anterior cruciate ligament reconstruction

2019· article· en· W2916194007 on OpenAlexaff
Scott Tulloch, Alan Getgood

Bibliographic record

VenueAnnals of Joint · 2019
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsFowler Kennedy Sport Medicine ClinicWestern University
Fundersnot available
KeywordsAnterolateral ligamentBiomechanicsAnterior cruciate ligamentAnterior cruciate ligament reconstructionKinematicsMedicineLigamentAnatomyOrthodonticsPhysics

Abstract

fetched live from OpenAlex

Anatomic intra-articular anterior cruciate ligament reconstruction (ACLR) techniques have been shown to reliably correct anteroposterior (translational) stability; however, they have failed to restore normal tibial rotational kinematics. Re-establishing rotational stability correlates with return to sport, functional scores, overall knee function and patient satisfaction. Several structures in addition to the ACL have been identified as important contributors to rotational knee stability. Recent interest in the anatomical and biomechanical properties of the anterolateral soft tissue structures has led to a resurgence in surgical techniques, specifically anterolateral ligament (ALL) reconstruction and lateral extra-articular tenodesis (LET), to address rotational stability at the time of ACL reconstruction. In the accompanying review we outline the relevant anatomy, biomechanics and clinical results for lateral augmentation procedures; discuss the indications and describe our preferred LET technique for lateral augmentation.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.327
Teacher spread0.292 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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