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Record W2522937400 · doi:10.1148/radiol.2016152320

Imaging of Athletic Injuries of Knee Ligaments and Menisci: Sports Imaging Series

2016· review· en· W2522937400 on OpenAlexaff
Ali Naraghi, Lawrence M. White

Bibliographic record

VenueRadiology · 2016
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsWomen's College Hospital
Fundersnot available
KeywordsMedicineSports injurySeries (stratigraphy)Sports medicineAnatomyPhysical therapy

Abstract

fetched live from OpenAlex

Acute knee injuries are a common source of morbidity in athletes and if overlooked may result in chronic functional impairment. Magnetic resonance (MR) imaging of the knee has become the most commonly performed musculoskeletal MR examination and is an indispensable tool in the appropriate management of the injured athlete. Meniscal and ligamentous tearing are the most frequent indications for surgical intervention in sports injuries and an understanding of the anatomy, biomechanics, mechanisms of injury, and patterns of injury are all critical to accurate diagnosis and appropriate management. These will be discussed in reference to meniscal tears and injuries of the cruciate ligaments as well as injuries of the posterolateral and posteromedial corners of the knee. (©) RSNA, 2016.

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)0.000
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 score0.985
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.307
Teacher spread0.297 · 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

Citations76
Published2016
Admission routes1
Has abstractyes

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