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Magnetic Resonance Imaging of Congenital, Inflammatory, and Infectious Soft-Tissue Lesions in Children

2002· review· en· W2325416164 on OpenAlex
Ricardo Faingold, Kamaldine Oudjhane, Derek Armstrong, Pedro A.B. Albuquerque

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTopics in Magnetic Resonance Imaging · 2002
Typereview
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMagnetic resonance imagingSoft tissueMedicineLipoatrophyLipodystrophyRadiologyPathologyAdipose tissueEdemaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Magnetic resonance imaging has the advantages of multiplanar capability and high degree of tissue differentiation. It is useful for assessing the extent of soft-tissue abnormalities, such as vascular malformations, inflammatory and infectious processes, muscle disorders, and limb hypertrophy. Magnetic resonance imaging is sensitive to the presence of water and edema and is a good indicator for early diagnosis of inflammation and its level of activity. Fat-saturation techniques, including T2-weighted sequences and inversion recovery imaging, optimize diagnostic accuracy. T1-weighted images are good at defining the distribution and proportion of fat in the body, so they are useful in evaluating syndromes of the limbs, including vascular malformations, as well as lipoatrophy-lipodystrophy conditions. Magnetic resonance imaging provides guidance for efficient tissue biopsy. It allows comprehensive pretherapeutic assessment of soft-tissue vascular anomalies. It constitutes a good modality for following up the natural history of soft-tissue disorders during childhood.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.307
Teacher spread0.288 · 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