MétaCan
Menu
Back to cohort
Record W1977083511 · doi:10.1097/rmr.0000000000000047

Elbow Magnetic Resonance Imaging

2015· review· en· W1977083511 on OpenAlex
Jennifer Hauptfleisch, Collette English, Darra Murphy

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTopics in Magnetic Resonance Imaging · 2015
Typereview
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMagnetic resonance imagingNuclear magnetic resonanceElbowMedicinePhysicsRadiology

Abstract

fetched live from OpenAlex

The elbow is a complex joint. Magnetic resonance imaging (MRI) is often the imaging modality of choice in the workup of elbow pain, especially in sports injuries and younger patients who often have either a history of a chronic repetitive strain such as the throwing athlete or a distinct traumatic injury. Traumatic injuries and alternative musculoskeletal pathologies can affect the ligaments, musculotendinous, cartilaginous, and osseous structures of the elbow as well as the 3 main nerves to the upper limb, and these structures are best assessed with MRI.Knowledge of the complex anatomy of the elbow joint as well as patterns of injury and disease is important for the radiologist to make an accurate diagnosis in the setting of elbow pain. This chapter will outline elbow anatomy, basic imaging parameters, compartmental pathology, and finally applications of some novel MRI techniques.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.338
Teacher spread0.296 · 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