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Record W2626675752 · doi:10.1002/jum.14276

Stress Fractures of the Calcaneus Diagnosed by Sonography: Report of 8 Cases

2017· article· en· W2626675752 on OpenAlexaff
Stefano Bianchi, Dien Hung Luong

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineMagnetic resonance imagingPeriosteumRadiologyStress fracturesCalcaneusPhysical examinationDifferential diagnosisAnkleRadiographySoft tissueSurgeryPathology

Abstract

fetched live from OpenAlex

We present a retrospective review of 8 patients (6 women and 2 men, with an age range of 46-80 years; mean age, 60.2 years) in whom sonography was used to diagnose a calcaneal stress fracture. Sonography was performed because of a clinical suspicion of soft tissue injuries. Two patients were first assessed by standard radiography; for the remaining patients, sonography was the first imaging technique used. Patients were subsequently examined by magnetic resonance imaging, except for 1 patient in whom the diagnosis was made only on a clinical-sonographic correlation. On sonography, there was thickening of the periosteum and subcutaneous edema in all patients; a calcified bone callus was evident in none of the 8 patients. Cortical irregularities were found in 6 of 8 patients. Color Doppler imaging showed local hypervascular changes of the periosteum in all patients. Sonography, together with clinical findings, can be used to diagnose a calcaneal stress fracture. We suggest that sonologists should include a calcaneal stress fracture in their differential diagnosis in cases of mechanical hind foot pain. They must also include, as a part of every sonographic examination of the ankle, an examination of the calcaneus and be aware of the sonographic appearance of stress fractures. If the diagnosis is still uncertain after the sonographic examination, magnetic resonance imaging should be prescribed.

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)0.001
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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.

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

Citations21
Published2017
Admission routes1
Has abstractyes

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