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Record W2762762594 · doi:10.1055/s-0037-1606134

Update on Pediatric Hip Imaging

2017· review· en· W2762762594 on OpenAlexaff
Filip Vanhoenacker, Jacob L. Jaremko, Lennart Jans, Nele Herregods

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2017
Typereview
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsMedicineMagnetic resonance imagingPhysical examinationRadiologyRadiographyMedical imagingDifferential diagnosisPathology

Abstract

fetched live from OpenAlex

Abstract Hip disorders are common in children. Prompt diagnosis and treatment are important because of the potential complications. Symptoms are frequently nonspecific, and clinical examination can be difficult and unreliable, especially in smaller children. Therefore, imaging can be valuable. Radiography and ultrasound remain the initial imaging modalities of choice. Increasingly, magnetic resonance imaging is obtained for assessing the pediatric hip, although the long imaging time and need for sedation may limit its use in daily practice. Because of the exposure to ionizing radiation, the use of computed tomography and bone scintigraphy in children is limited to selected cases. Pediatric hip pathology varies depending on patient age. This article provides an overview of common hip pathologies in children including congenital and developmental pathologies, trauma, infectious processes, inflammatory disease, and neoplasm. The age of the child, history, and clinical examination are essential to narrow down the differential diagnosis and subsequent selection of the appropriate imaging modality.

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)0.003
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.034
GPT teacher head0.383
Teacher spread0.348 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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