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Record W3040260734 · doi:10.1002/jmri.27251

Benign Neoplasms, <scp>Mass‐Like</scp> Infections, and Pseudotumors That Mimic Hepatic Malignancy at <scp>MRI</scp>

2020· review· en· W3040260734 on OpenAlex
Andreu F. Costa, Sharon E. Clarke, Ashley Stueck, Matthew D. F. McInnes, Seng Thipphavong

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

VenueJournal of Magnetic Resonance Imaging · 2020
Typereview
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsWomen's College HospitalMount Sinai HospitalOttawa HospitalQueen Elizabeth II Health Sciences CentreUniversity of OttawaUniversity of TorontoUniversity Health NetworkDalhousie University
Fundersnot available
KeywordsMalignancyMedicinePathologyHepatocellular carcinomaHepatic fibrosisInflammatory pseudotumorFocal nodular hyperplasiaHepatocellular adenomaRadiologyFibrosisAdenomaInternal medicineLesion

Abstract

fetched live from OpenAlex

A variety of conditions may mimic hepatic malignancy at MRI. These include benign hepatic tumors and tumor-like entities such as focal nodular hyperplasia-like lesions, hepatocellular adenoma, hepatic infections, inflammatory pseudotumor, vascular entities, and in the cirrhotic liver, confluent fibrosis, and hypertrophic pseudomass. These conditions demonstrate MRI features that overlap with hepatic malignancy, and can be challenging for radiologists to diagnose accurately. In this review we discuss the MRI manifestations of various conditions that mimic hepatic malignancy, and highlight features that may allow distinction from malignancy. Level of Evidence 5 Technical Efficacy Stage 3.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.815
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.026
GPT teacher head0.294
Teacher spread0.268 · 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