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Record W2055849361 · doi:10.1097/jsa.0000000000000046

Physical Examination and Imaging of the Lateral Collateral Ligament and Posterolateral Corner of the Knee

2015· review· en· W2055849361 on OpenAlexaff
Brian M. Devitt, Daniel B. Whelan

Bibliographic record

VenueSports Medicine and Arthroscopy Review · 2015
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineLigamentCollateralMedial collateral ligamentPhysical examinationAnatomyRadiology

Abstract

fetched live from OpenAlex

The initial assessment of injury to the lateral collateral ligament and posterolateral corner is often challenging, particularly in the context of a multiligamentous injury. Although advanced imaging techniques have enhanced the evaluation of knee injuries, the significant, and often unique, contribution of clinical examination should not be overlooked. Clinical examination starts with a thorough history, which is instrumental in elucidating not only the patient's symptomatology but also the mechanism of injury. Differentiating between acute and chronic injury and teasing out the patient's functional limitations are instructive in defining the appropriate treatment plan. The treating physician needs patience, vigilance, and a variety of diagnostic tools to reach a precise diagnosis. Each injury should be approached in a methodical and systematic manner to ensure an accurate initial assessment. This review provides a step-wise approach to the clinical assessment of the lateral collateral ligament and posterolateral corner injured knee. Adjunctive imaging modalities and investigations are also discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.341
Teacher spread0.318 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations32
Published2015
Admission routes1
Has abstractyes

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