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Record W2163218297 · doi:10.1148/rg.337125167

Plantar Tendons of the Foot: MR Imaging and US

2013· review· en· W2163218297 on OpenAlexaff
Andrea Donovan, Zehava Sadka Rosenberg, Jenny T. Bencardino, Zoraida Restrepo Velez, David B. Blonder, Gina A. Ciavarra, Ronald S. Adler

Bibliographic record

VenueRadiographics · 2013
Typereview
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineTendinosisTendonTenosynovitisMagnetic resonance imagingForefootPlantar fasciaAchilles tendonUltrasoundPlantar fasciitisAnatomyRadiologyTendinopathySurgeryHeel

Abstract

fetched live from OpenAlex

Tendon disorders along the plantar aspect of the foot may lead to significant symptoms but are often clinically misdiagnosed. Familiarity with the normal anatomy of the plantar tendons and its appearance at magnetic resonance (MR) imaging and ultrasonography (US) is essential for recognizing plantar tendon disorders. At MR imaging, the course of the plantar tendons is optimally visualized with dedicated imaging of the midfoot and forefoot. This imaging should include short-axis images obtained perpendicular to the long axis of the metatarsal shafts, which allows true cross-sectional evaluation of the plantar tendons. Normal plantar tendons appear as low-signal-intensity structures with all MR sequences. At US, accurate evaluation of the tendons requires that the ultrasound beam be perpendicular to the tendon. The normal tendon appears as a compact linear band of echogenic tissue that contains a fine, mixed hypoechoic and hyperechoic internal fibrillar pattern. Tendon injuries can be grouped into six major categories: tendinosis, peritendinosis, tenosynovitis, entrapment, rupture, and instability (subluxation or dislocation) and can be well assessed with both MR imaging and US. The radiologist plays an important role in the diagnosis of plantar tendon disorders, and recognizing their imaging appearances at MR imaging and US is essential.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.025
GPT teacher head0.296
Teacher spread0.271 · 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 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

Citations32
Published2013
Admission routes1
Has abstractyes

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