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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 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.019
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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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