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Record W4290861467 · doi:10.1148/rg.220055

US and MRI of Pelvic Tendon Anatomy and Pathologic Conditions

2022· article· en· W4290861467 on OpenAlexaff
Dyan V. Flores, Darwin Fernández Umpire, Marcos Loreto Sampaio, Mark Cresswell, Mini N. Pathria

Bibliographic record

VenueRadiographics · 2022
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicinePelvisTendinosisAnatomyPubic symphysisTendonTearsMagnetic resonance imagingRadiologyTendinopathySurgery

Abstract

fetched live from OpenAlex

The bony pelvis serves as the attachment site for a large number of powerful muscles and tendons that drive lower extremity movement. Organizing the pelvic tendons into groups that share a common function and anatomic location helps the radiologist systematically evaluate these structures for injury, which can be caused by repetitive stress, acute trauma, or failure of degenerated tissues. Tears of the anteromedial adductors around the pubic symphysis and anterior flexors traversing anterior to the hip principally affect younger male athletes. Tears of the lateral abductors and posterior extensors are more common in older individuals with senescent tendinosis. The deep external rotators are protected and rarely injured, although they can be impinged. Imaging of the pelvic tendons relies primarily on US and MRI; both provide high spatial and contrast resolution for soft tissues. US offers affordable point-of-care service and dynamic assessment, while MRI allows simultaneous osseous and articular evaluation and is less operator dependent. While the imaging findings of pelvic tendon injury mirror those at appendicular body sites, radiologists may be less familiar with tendon anatomy and pathologic conditions at the pelvis. The authors review pertinent anatomy and imaging considerations and illustrate common injuries affecting the pelvic tendons. Online supplemental material is available for this article. ©RSNA, 2022

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.212

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.267
Teacher spread0.261 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2022
Admission routes1
Has abstractyes

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