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Record W2090899270 · doi:10.1002/jmri.22672

MR imaging of the postoperative knee

2011· review· en· W2090899270 on OpenAlexaff
Ralph Gnannt, Avneesh Chhabra, John Theodoropoulos, Juerg Hodler, Gustav Andreisek

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2011
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineOrthopedic surgeryArthroscopyMeniscusMedial meniscusMedial collateral ligamentLateral meniscusAnterior cruciate ligamentKnee JointMagnetic resonance imagingSurgeryRadiologyLigamentPosterior cruciate ligamentOsteoarthritis

Abstract

fetched live from OpenAlex

Advances in orthopedic and arthroscopic surgical procedures of the knee such as, knee replacement, ligamentous reconstruction as well as articular cartilage and meniscus repair techniques have resulted in a significant increase in the number of patients undergoing knee arthroscopy or open surgery. As a consequence postoperative MR imaging examinations increase. Comprehensive knowledge of the normal postoperative MR imaging appearances and abnormal findings in the knee associated with failure or complications of common orthopedic and arthroscopic surgical procedures currently undertaken is crucial. This article reviews the various normal and pathological postoperative MR imaging findings following anterior and posterior cruciate ligament, medial collateral ligament and posterolateral corner reconstruction, meniscus and articular cartilage surgery as well as total knee arthroplasty with emphasis on those surgical procedures which general radiologists will likely be faced in their daily clinical routine.

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 score0.994
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.307
Teacher spread0.288 · 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

Citations38
Published2011
Admission routes1
Has abstractyes

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