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Record W3028587194 · doi:10.1055/s-0040-1710066

Ultrasound Imaging Guide for Assessment of the Intrinsic Ligaments Stabilizing the Subtalar and Midtarsal Joints

2020· review· en· W3028587194 on OpenAlexaff
Federico Zaottini, Riccardo Picasso, Federico Pistoia, Maribel Miguel‐Pérez, Ingrid Möller, Federica Rossi, Alessandra Bruns, Alberto Tagliafico, Carlo Martinoli

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2020
Typereview
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineSubtalar jointUltrasoundLigamentAnatomyTarsus (eyelids)AnkleBiomechanicsUltrasound imagingTearsRadiologySurgery

Abstract

fetched live from OpenAlex

Ligament injuries around the subtalar, talocalcaneonavicular, and calcaneocuboid joints are often underestimated on clinical and imaging findings during investigation of patients with ankle and foot injuries. Because a delayed diagnosis of midtarsal ligament tears may lead to chronic pain and functional disability, an in-depth knowledge of the complex regional anatomy and of the appropriate ultrasound scanning technique is a prerequisite for evaluating these structures and avoiding misdiagnoses. The objective of this article is twofold: to describe the relevant anatomy and biomechanics related to the ligaments that stabilize the subtalar, talocalcaneonavicular, and calcaneocuboid joints, and to illustrate reasoned landmark-based scanning techniques to provide a systematic examination of these ligaments and thus make ultrasound an effective tool for assessment of patients with suspected subtalar or midtarsal sprain.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.012

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 head0.027
GPT teacher head0.356
Teacher spread0.330 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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