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Record W2022571054 · doi:10.1055/s-0029-1237689

Imaging of Benign Pediatric Soft Tissue Tumors

2009· review· en· W2022571054 on OpenAlexaff
Oscar M. Navarro

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2009
Typereview
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineSoft tissueMagnetic resonance imagingRadiologyPresentation (obstetrics)RadiographyUltrasonographyModality (human–computer interaction)Physical examinationPathology

Abstract

fetched live from OpenAlex

There is a broad spectrum of benign soft tissue tumors in children, including, among others, vascular, fibroblastic/myofibroblastic, and adipocytic lesions as well as pseudotumors. The diagnosis of superficial soft tissue tumors is in many instances made clinically, whereas those with more equivocal presentation and those found in the deeper soft tissues often require imaging. Ultrasonography is the modality of choice for smaller and superficial lesions and is particularly useful for vascular tumors. Magnetic resonance imaging is the modality of choice for the larger and deeper lesions and for those in which ultrasonography is not adequate. Plain radiographs and computed tomography have a very limited role in the evaluation of soft tissue masses. Correlation with clinical history and findings on physical examination are imperative for an appropriate interpretation of the imaging findings. Although certain entities can be confidently diagnosed based on the combination of clinical and imaging findings, histology is frequently required for a definitive diagnosis. The differentiation of benign from malignant tumors is also challenging and often not possible based on the imaging findings. This article reviews the clinical features and imaging findings of the most common benign pediatric soft tissue tumors.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.018
GPT teacher head0.344
Teacher spread0.327 · 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 designOther design
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

Citations18
Published2009
Admission routes1
Has abstractyes

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