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Record W4214515931 · doi:10.1186/s13244-022-01177-9

Multiparametric MRI evaluation of bone sarcomas in children

2022· review· en· W4214515931 on OpenAlex

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

VenueInsights into Imaging · 2022
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineOsteosarcomaSarcomaNeuroradiologyRadiologyRhabdomyosarcomaMagnetic resonance imagingBone SarcomaPathologyNeurology

Abstract

fetched live from OpenAlex

Osteosarcoma and Ewing sarcoma are the most common bone sarcomas in children. Their clinical presentation is very variable depending on the age of the patient and tumor location. MRI is the modality of choice to assess these bone sarcomas and has an important function at diagnosis and also for monitoring recurrence or tumor response. Anatomic sequences include T1- and T2-weighted images and provide morphological assessment that is crucial to localize the tumor and describe anatomical boundaries. Multiparametric MRI provides functional information that helps in the assessment of tumor response to therapy by using different imaging sequences and biomarkers. This review manuscript illustrates the role of MRI in osteosarcoma and Ewing sarcoma in the pediatric population, with emphasis on a functional perspective, highlighting the use of diffusion-weighted imaging and dynamic contrast-enhanced MRI at diagnosis, and during and after treatment.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.076
GPT teacher head0.381
Teacher spread0.305 · 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