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Record W2799575446 · doi:10.1097/bto.0000000000000310

Anterolateral Complex Reconstruction: Another Fad or Method to Improve ACL Outcomes?

2018· article· en· W2799575446 on OpenAlexaff
Ryan Wood, Jacquelyn Marsh, Alan Getgood

Bibliographic record

VenueTechniques in Orthopaedics · 2018
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsWestern UniversityFowler Kennedy Sport Medicine Clinic
Fundersnot available
KeywordsMedicineAnterolateral ligamentAnterior cruciate ligament reconstructionAnterior cruciate ligamentBiomechanicsSurgeryPhysical medicine and rehabilitationOrthodonticsAnatomy

Abstract

fetched live from OpenAlex

Anterolateral rotational laxity of the knee is a persistent problem following anterior cruciate ligament reconstruction (ACLR) that can lead to increased rates of graft failure. Renewed interest in the anterolateral complex of the knee has led to a resurgence in the use of adjunctive techniques such as lateral extra-articular tenodesis and anterolateral ligament reconstruction. Use of these techniques can restore normal knee kinematics and potentially thereby reduce the rate of graft failure. Historically, experience with modified ACLR techniques such as the double-bundle ACLR have shown that improved biomechanics is not always reflected in clinical outcome trials. Additional procedures also come with additional costs and further economic analysis needs to be performed to clarify whether these additional costs are offset by improved clinical and societal outcomes in the longer-term.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.029
GPT teacher head0.367
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 designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2018
Admission routes1
Has abstractyes

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