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Record W2026298531 · doi:10.1055/s-0029-1242191

MR Imaging of the Extensor Mechanism

2009· review· en· W2026298531 on OpenAlexaff
Josef Kramer, Lawrence M. White, Michael P. Recht

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2009
Typereview
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsWomen's College HospitalUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineMagnetic resonance imagingPathologicalMechanism (biology)Physical examinationRadiographyRadiologyPhysical medicine and rehabilitationPathology

Abstract

fetched live from OpenAlex

Sports injuries of the knee involving the extensor mechanism are common. Specific conditions additionally affect the extensor mechanism in adolescents during growth and maturation, and in older patients as a result of degeneration. Prior to the advent of magnetic resonance (MR) imaging, the specific anatomical-pathological cause of anterior knee pain was often uncertain, with diagnosis limited to clinical examination and conventional radiographic assessment. MR imaging allows for the accurate and specific diagnosis of pathology of the extensor mechanism, impacting on decision making and clinical treatment of such conditions. In this article the anatomy, normal MR appearance, and common pathological conditions of the extensor mechanism of the knee are described.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.012
GPT teacher head0.327
Teacher spread0.315 · 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
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

Citations17
Published2009
Admission routes1
Has abstractyes

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