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Record W3210579035 · doi:10.1055/s-0041-1735468

Pediatric Imaging of the Elbow: A Pictorial Review

2021· review· en· W3210579035 on OpenAlexaff
Tim Vanmarsnille, Frederiek Laloo, Nele Herregods, Jacob L. Jaremko, Koenraad Verstraete, Lennart Jans

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2021
Typereview
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElbowMedicineMagnetic resonance imagingRadiographyRadiologySoft tissueModality (human–computer interaction)

Abstract

fetched live from OpenAlex

The elbow is a complex joint, subject to a wide range of traumatic, inflammatory, metabolic and neoplastic insults. The pediatric elbow has several diagnostic pitfalls due to the normal developmental changes in children. Knowledge of these normal variants is essential for both diagnosis and management of their elbow injuries. Radiography remains the first imaging modality of choice. Magnetic resonance imaging is excellent in evaluating lesions within the bone and soft tissues. In this pictorial essay, we provide insights into pediatric elbow imaging, show a range of entities specific to the pediatric elbow, and discuss diagnostic pitfalls that result from normal elbow growth in children.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.888
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.345
Teacher spread0.326 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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