MétaCan
Menu
Back to cohort
Record W2128881570 · doi:10.1148/rg.304095188

MR Imaging of Entrapment Neuropathies of the Lower Extremity

2010· article· en· W2128881570 on OpenAlex

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

VenueRadiographics · 2010
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineEntrapment NeuropathyMagnetic resonance neurographyAnkleSuperficial peroneal nerveEntrapmentMagnetic resonance imagingCommon peroneal nerveTibial nerveSural nerveAnatomyRadiologySurgeryCarpal tunnel syndrome

Abstract

fetched live from OpenAlex

Entrapment neuropathies of the knee, leg, ankle, and foot are often underdiagnosed, as the results of clinical examination and electrophysiologic evaluation are not always reliable. The causes of most entrapment neuropathies in the lower extremity may be divided into two major categories: (a) mechanical causes, which occur at fibrous or fibro-osseous tunnels, and (b) dynamic causes related to nerve injury during specific limb positioning. Magnetic resonance (MR) imaging, including high-resolution MR neurography, allows detailed evaluation of the course and morphology of peripheral nerves, as well as accurate delineation of surrounding soft-tissue and osseous structures that may contribute to nerve entrapment. Familiarity with the normal MR imaging anatomy of the nerves in the knee, leg, ankle, and foot is essential for accurate assessment of the presence of peripheral entrapment syndromes. Common entrapment neuropathies in the knee, leg, ankle, and foot include those of the common peroneal nerve, deep peroneal nerve, superficial peroneal nerve, tibial nerve and its branches, and sural nerve.

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)0.000
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 score0.180
Threshold uncertainty score0.270

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 head0.007
GPT teacher head0.234
Teacher spread0.227 · 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