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Record W2088647037 · doi:10.1055/s-0030-1254523

Imaging of Neuropathies about the Ankle and Foot

2010· review· en· W2088647037 on OpenAlexaff
Carlo Martinoli, Michel Court‐Payen, Johan Michaud, Luca Padua, Luisa Altafini, Alessandra Marchetti, Maribel Miguel‐Pérez, Maura Valle, C Hovgaard, Micael Haugegaard, Alberto Tagliafico

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2010
Typereview
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineAnkleMagnetic resonance imagingEntrapment NeuropathyPhysical examinationEtiologyMagnetic resonance neurographyPhysical medicine and rehabilitationAnatomyRadiologyPathologyCarpal tunnel syndrome

Abstract

fetched live from OpenAlex

Neuropathies about the ankle and foot may be the cause of chronic pain and disability. In most cases, these conditions derive from mechanical or dynamic compression of a segment of a nerve within a narrow osteofibrous tunnel, an opening in a fibrous structure, or a passageway close to a ligament or a muscle. Although the evaluation of nerve disorders primarily relies on neurological examination and electrophysiology, diagnostic imaging is currently used as a complement to help define the site and etiology of nerve compression and exclude other disease possibly underlying the patient' symptoms. In this article, a review of the anatomical and pathological features of nerve entrapments in the distal lower extremity is presented on ultrasound and magnetic resonance imaging, according to the nerve involved. KEYWORDS Tibial nerve - superficial peroneal nerve - deep peroneal nerve - sural nerve - plantar nerves - interdigital nerves - ankle and foot neuropathies - nerve disorders - ultrasound - magnetic resonance imaging

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.015
GPT teacher head0.327
Teacher spread0.311 · 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

Citations27
Published2010
Admission routes1
Has abstractyes

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